Graph of TEs
Setup
# library(ggplot2)
# library(reshape2)
library(ggpattern)
library(viridis)
library(colorRamps)
library(gridExtra)
library(ggplot2)
library('igraph')
library(ggnet)
library(network)
library(khroma)
library(dplyr)
library(ggpubr)
library(Biostrings)Warning: package ‘S4Vectors’ was built under R version 4.2.2Warning: package ‘GenomeInfoDb’ was built under R version 4.2.2source('similarity.R')
source('seqdotplot.R')
source('msaplot.R')
sunset <- colour("sunset")
discrete_rainbow <- colour("discrete rainbow")
path.base = '../../../'
path.work = paste(path.base, '02_analysis/04_sv/01_data/', sep = '')
path.tair = paste(path.base, '01_data_common/01_tair10/', sep = '')
path.figures = paste(path.base, '02_analysis/04_sv/03_figures/', sep = '')
path.svs = paste(path.base, '01_data_common/02_annot_denovo/02_pannagram/svs/', sep = '')
# path.genes = paste(path.base, '01_data_common/02_annot_denovo/02_pannagram/genes/', sep = '')
# sim.cutoff = 0.9
sim.cutoff = 0.85
Colors
# ---- Colors of TE content ----
te.content.names = c("noTE", "isTE", "hasTE", "hasTEpart", "isTEpart")
te.cols = c('#D8D9CF', '#EB455F', '#7B6079', '#3C8DAD', '#79B773')
names(te.cols) = te.content.names
# ---- Colors of BLAST hits ----
colors.blast.hits <- c("in graph" = "#676FA3",
"partial overlap" = "#FF9F29",
"1 self-hit" = "#6EBF8B",
"0 hits" = "#D82148",
"in graph but not in SVs" = "#151D3B")
# ---- Colors of known TE families ----
fam.palette = c()
fam.palette['Unassigned'] = 'grey'
fam.palette['Mix'] = 'grey20'
fam.palette['Mix with Helitron'] = '#266D98'
fam.palette['Helitron'] = '#BCACDE'
fam.palette["LTR/Copia"] = '#BFDB38'
fam.palette["LTR/Gypsy"] = '#54B435'
fam.palette["DNA/HAT"] = '#F9B5D0'
fam.palette["DNA+"] = '#C8658C'
fam.palette["DNA"] = '#C8658C'
fam.palette["DNA/MuDR"] = '#971549'
fam.palette["LINE"] = '#FFC26F'
fam.palette["RathE1/2/3_cons"] = '#C38154'
fam.palette["SINE"] = '#884A39'
fam.palette["TEG"] = '#4E3636'
# Colors for new TEs
g.cols = discrete_rainbow(length(unique(sv.memb$prot)))Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'unique': object 'sv.memb' not foundTEs
# Load similarity function
bl.file = paste(path.work,'new_te_on_te.fasta',sep = '')
bl.res = read.table(bl.file)
bl.res = bl.res[bl.res$V1 != bl.res$V8,]
bl.res.init = bl.res
bl.res = bl.res[bl.res$V6 >= sim.cutoff * 100,]
res.nest = findNestedness(bl.res, use.strand = F)[1] 130447
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[1] 0res.nest.len = sapply(unique(c(res.nest$V1, res.nest$V8)), function(s) as.numeric(strsplit(s, '\\|')[[1]][5]))
res.nest$len1 = res.nest.len[res.nest$V1]
res.nest$len8 = res.nest.len[res.nest$V8]
res.nest$p1 = res.nest$C1 / res.nest$len1
res.nest$p8 = res.nest$C8 / res.nest$len8
res.nest.sim = res.nest[(res.nest$p1 >= sim.cutoff) |
(res.nest$p8 >= sim.cutoff),]How many TEs are in the graph
Distribution among families and subfamilies Distribution among lengths
te.in.graph = unique(c(res.nest$V1, res.nest$V8))
# What is the actual number of TEs
file.content <- readLines(bl.file)
selected.lines <- file.content[grepl("^# Query:|hits found", file.content)]
df.query = data.frame(b.query=selected.lines[seq(1, length(selected.lines), by = 2)],
b.hits=selected.lines[seq(2, length(selected.lines), by = 2)])
df.query$query <- gsub("^# Query: (.*)", "\\1", df.query$b.query)
df.query$len <- as.numeric(sapply(strsplit(df.query$query, "\\|"), function(x) x[5]))
df.query$hits <- as.numeric(stringr::str_extract(df.query$b.hits, "\\d+"))
df.query$val.hits = df.query$hits
df.query$val.hits[df.query$val.hits >= 2] = 2
df.query$val.hits[df.query$query %in% bl.res$V8] = 2
df.query$val.hits[df.query$query %in% te.in.graph] = 3
hit.values = c('0 hits', '1 self-hit', 'partial overlap', 'in graph', "in graph but not in SVs")
df.query$s.hits = hit.values[df.query$val.hits+1]
df.query$s.hits = factor(df.query$s.hits, levels = rev(hit.values))
df.query$family <- sapply(strsplit(df.query$query, "\\|"), function(x) x[9])
df.query$subfam <- sapply(strsplit(df.query$query, "\\|"), function(x) x[8])
# TEs, which are not in SVs
te.in.svs = read.table(paste(path.work, 'blast_tes_on_sv.txt', sep = ''), stringsAsFactors = F)
te.rest = setdiff(df.query$query, te.in.svs$V1)
te.in.svs = read.table(paste(path.work, 'blast_sv_on_tes.txt', sep = ''), stringsAsFactors = F)
te.rest = setdiff(te.rest, te.in.svs$V8)
df.query$s.hits[df.query$query %in% te.rest] = "in graph but not in SVs"
p = ggplot(df.query, aes(x = len, fill = s.hits, color = s.hits)) +
# geom_histogram(aes(y = ..density..), alpha=0.5, color = "black", bins = 30) +
# geom_jitter(height = 0.02, width = 0, alpha = 0.7) +
geom_density(alpha = 0.5) +
scale_fill_manual(values = colors.blast.hits) +
scale_color_manual(values = colors.blast.hits) +
scale_x_log10() +
labs(fill = NULL, color = NULL) +
xlab('length of TEs') + ylab('Normalised density') +
theme_minimal() +
theme(legend.position = c(1, 1), legend.justification = c(1, 1),
legend.background = element_rect(color = "grey90"))
p
pdf(paste(path.figures, 'tes_self_blast_len_density.pdf', sep = ''), width = 5, height = 4)print(p) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2 
table(df.query$val.hits)
0 1 2 3
1584 13615 766 19125 TEs not in SVs
# TEs in te-graph: te.in.graph
# TEs which are have no connection to SVs
df = as.data.frame(table(df.query$s.hits))
p = ggplot(df, aes(x = "", y = Freq, fill = Var1)) +
geom_bar(stat="identity", width=1, alpha = 0.7) +
coord_polar("y", start=0) +
labs(title=NULL, fill="Categories") +
theme_void()+
scale_fill_manual(values = colors.blast.hits) +
geom_text(aes(label = Freq,x = 1.3), position = position_stack(vjust = 0.5)) + theme(legend.position="none")
p
pdf(paste(path.figures, 'tes_self_blast_pie_chart.pdf', sep = ''), width = 3, height = 3)print(p) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2
Examples
Examples no hits
pdf(paste(path.figures, 'tes_self_scatter_no_hits_long.pdf', sep = ''), width = 5, height = 4)
print(p) # Plot 1 --> in the first page of PDF
dev.off()
null device
1
head(df.query.tmp[df.query.tmp$family == 'DNA/MuDR',]$query)[1] "te|641277|641420|1|144|+|AT1TE02080|ARNOLDY1|DNA/MuDR"
[2] "te|1157763|1157863|1|101|+|AT1TE03780|LIMPET1|DNA/MuDR"
[3] "te|4546279|4546388|1|110|+|AT1TE14750|ARNOLD2|DNA/MuDR"
[4] "te|6753991|6754119|1|129|-|AT1TE21830|ATDNAI27T9A|DNA/MuDR"
[5] "te|10655834|10655963|1|130|+|AT1TE34455|ARNOLD1|DNA/MuDR"
[6] "te|11660991|11661122|1|132|+|AT1TE37760|ATDNA2T9C|DNA/MuDR"Examples one self-hit
families
df.query.tmp = df.query[(df.query$val.hits == 1),]
cnt.init = c(table(df.query$family))
cnt.tmp = c(table(df.query.tmp$family))
common_names <- intersect(names(cnt.init), names(cnt.tmp))
# Создание dataframe только для совпадающих имен
df_match <- data.frame(names = common_names, values.init = cnt.init[common_names],
values.tmp = cnt.tmp[common_names])
gradient_colors <- c(discrete_rainbow(nrow(df_match)))
names(gradient_colors) = NULL
p = ggplot(df_match, aes(x = values.init, y = values.tmp, label = names, color = names)) +
geom_point() +
# geom_text(hjust = 0, vjust = 0) +
ggrepel::geom_text_repel(max.overlaps = 20) +
xlab("Initial counts") +
ylab("Counts in \"1 self-hits\" category") +
scale_x_log10() +
scale_y_log10() +
scale_color_manual(values = gradient_colors) +
theme(legend.position = "none") +
guides(color = FALSE) +
theme_minimal()
p
pdf(paste(path.figures, 'tes_self_scatter_1_selfhits_fam.pdf', sep = ''), width = 5, height = 4)print(p) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2
subfamilies
df.query.tmp = df.query[(df.query$val.hits == 1) & (df.query$len >= 600),]
cnt.init = c(table(df.query$subfam))
cnt.tmp = c(table(df.query.tmp$subfam))
common_names <- intersect(names(cnt.init), names(cnt.tmp))
# Создание dataframe только для совпадающих имен
df_match <- data.frame(names = common_names, values.init = cnt.init[common_names],
values.tmp = cnt.tmp[common_names])
# gradient_colors <- c(discrete_rainbow(nrow(df_match)))
names(gradient_colors) = NULL
p = ggplot(df_match, aes(x = values.init, y = values.tmp, label = names, color = names)) +
geom_point() +
# geom_text(hjust = 0, vjust = 0) +
ggrepel::geom_text_repel(max.overlaps = 20) +
xlab("Initial counts") +
ylab("Counts in \"1 self-hits\" category") +
scale_x_log10() +
scale_y_log10() +
# scale_color_manual(values = gradient_colors) +
theme(legend.position = "none") +
guides(color = FALSE) +
theme_minimal()
p
pdf(paste(path.figures, 'tes_self_scatter_1_selfhits_subfam.pdf', sep = ''), width = 7, height = 5)print(p) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2
individuals from subfamilies
s.subfam = 'ATREP8'
df.query.tmp = df.query[(df.query$subfam == s.subfam) & (df.query$len >= 600),]
df.query.tmp
Creating the graph
# all edges
idx = res.nest$p1 >= sim.cutoff
edges = cbind(res.nest$V1[idx], res.nest$V8[idx])
idx = res.nest$p8 >= sim.cutoff
edges = rbind(edges, cbind(res.nest$V8[idx], res.nest$V1[idx]))
te.enges.names = unique(c(edges[,1], edges[,2]))
te.enges.fam = sapply(te.enges.names, function(s) strsplit(s, '\\|')[[1]][9] )
te.enges.fam[te.enges.fam %in% c('DNA/Pogo', 'DNA/Tc1', 'DNA/Harbinger', 'DNA/En-Spm',
'DNA/HAT', 'DNA', 'DNA/Mariner')] = 'DNA'
te.enges.fam[te.enges.fam %in% c('RathE1_cons', 'RathE2_cons', 'RathE3_cons')] = 'RathE1/2/3_cons'
te.enges.fam[te.enges.fam %in% c('LINE/L1', 'LINE?')] = 'LINE'
te.enges.fam[te.enges.fam %in% c('Unassigned')] = 'Mix'
te.enges.fam[te.enges.fam %in% c('RC/Helitron')] = 'Helitron'
edges = edges[te.enges.fam[edges[,1]] != 'TEG',]
edges = edges[te.enges.fam[edges[,2]] != 'TEG',]
te.enges.names = unique(c(edges[,1], edges[,2]))
# nodes
idx = (res.nest$p1 >= sim.cutoff) & (res.nest$p8 >= sim.cutoff)
te.nodes = cbind(res.nest$V1[idx], res.nest$V8[idx])
te.nodes = te.nodes[te.enges.fam[te.nodes[,1]] != 'TEG',]
te.nodes = te.nodes[te.enges.fam[te.nodes[,2]] != 'TEG',]
te.rest = setdiff(te.enges.names, c(te.nodes[,1], te.nodes[,2]))
te.nodes.graph <- igraph::make_graph(t(te.nodes), directed = T)
te.nodes.graph <- igraph::simplify(te.nodes.graph)
te.nodes.comp <- igraph::components(te.nodes.graph)
nodes = data.frame(node = paste('N', te.nodes.comp$membership, sep = ''),
te = names(te.nodes.comp$membership))
nodes.rest = data.frame(node = paste('R', (1:length(te.rest)), sep = ''), te = te.rest)
nodes = rbind(nodes, nodes.rest)
rownames(nodes) = nodes$te
nodes.cnt = data.frame(cnt = c(table(nodes$node)))
nodes.cnt$node = rownames(nodes.cnt)
nodes.cnt$fam = sapply(nodes.cnt$node, function(s){
s.te = nodes$te[nodes$node == s]
fam.te = unique(te.enges.fam[s.te])
if(length(fam.te) == 1){
return(fam.te)
} else {
fam.te = setdiff(fam.te, 'TEG')
if(length(fam.te) == 1) return(fam.te)
return('Mix')
}
})
table(nodes.cnt$fam)
DNA DNA/MuDR Helitron LINE LTR/Copia LTR/Gypsy Mix
1109 1228 2302 356 503 1837 67
RathE1/2/3_cons SINE
53 27 # Redefine edges but with node names
idx.endes = (edges[,1] %in% nodes$te) & (edges[,2] %in% nodes$te)
b.graph = cbind(nodes[edges[idx.endes,1], 'node'],nodes[edges[idx.endes,2], 'node'])
b.graph = unique(b.graph)
# b.graph = b.graph[b.graph[,1] != b.graph[,2],]
b.graph.uni = b.graph[b.graph[,1] == b.graph[,2],]
b.graph = b.graph[b.graph[,1] != b.graph[,2],]
length(unique(c(b.graph[,1], b.graph[,2])))[1] 7245# reduce indirect arrows
idx.remove = c()
for(i.edge in 1:nrow(b.graph)){
if(i.edge %% 1000 == 0) print(i.edge)
tmp.to = b.graph[b.graph[,1] == b.graph[i.edge,1],2]
tmp.from = b.graph[b.graph[,2] == b.graph[i.edge,2],1]
if(length(intersect(tmp.to, tmp.from)) > 0) idx.remove = c(idx.remove, i.edge)
}[1] 1000
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[1] 20000idx.remove = unique(idx.remove)
b.graph = b.graph[-idx.remove,]
# b.graph = rbind(b.graph, b.graph.uni)
# Print graph
g.nodes.fam = nodes.cnt$fam
names(g.nodes.fam) = nodes.cnt$node
g.nodes.cnt = nodes.cnt$cnt
names(g.nodes.cnt) = nodes.cnt$node
g.cols = discrete_rainbow(length(unique(g.nodes.fam)))
names(g.cols) = unique(g.nodes.fam)
b.graph.init = b.graph
g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)Old colors
p <- ggnet2(g.part, label = F, edge.color = "black",
node.size = g.nodes.cnt[b.graph.names],
color = g.nodes.fam[b.graph.names],
palette = g.cols,
# mode = "kamadakawai"
) Loading required package: sna
Loading required package: statnet.common
Attaching package: ‘statnet.common’
The following objects are masked from ‘package:base’:
attr, order
sna: Tools for Social Network Analysis
Version 2.7-1 created on 2023-01-24.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
For citation information, type citation("sna").
Type help(package="sna") to get started.
Attaching package: ‘sna’
The following objects are masked from ‘package:igraph’:
betweenness, bonpow, closeness, components, degree, dyad.census, evcent, hierarchy, is.connected,
neighborhood, triad.census
Loading required package: scales
Attaching package: ‘scales’
The following object is masked from ‘package:viridis’:
viridis_pal
Warning: 'length(x) = 6909 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 6909 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 6909 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 6909 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 6909 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 6909 > 1' in coercion to 'logical(1)'p + guides(size = F)
#
# b.graph.fam = cbind(g.nodes.fam[b.graph[,1]], g.nodes.fam[b.graph[,2]])
# b.graph.fam
#
# which((b.graph.fam[,1] == 'DNA/MuDR') & (b.graph.fam[,1] == 'LINE'))
New Family colors
# g.fam.names = sort(unique(g.nodes.fam))
# fam.palette = c()
# idx.pallete = c()
#
# idx.fam <- grep("^Helitron", g.fam.names, value = FALSE)
# tmp.palette <- colorRampPalette(c('#BFACE2', '#266D98', '#422B72'))(length(idx.fam))
# idx.pallete = c(idx.pallete, idx.fam)
# fam.palette = c(fam.palette, tmp.palette)
#
# idx.fam <- grep("^LTR", g.fam.names, value = FALSE)
# tmp.palette <- colorRampPalette(c('#BFDB38', '#54B435'))(length(idx.fam))
# idx.pallete = c(idx.pallete, idx.fam)
# fam.palette = c(fam.palette, tmp.palette)
#
# idx.fam <- grep("^DNA", g.fam.names, value = FALSE)
# tmp.palette <- colorRampPalette(c('#F9B5D0', '#971549'))(length(idx.fam))
# idx.pallete = c(idx.pallete, idx.fam)
# fam.palette = c(fam.palette, tmp.palette)
#
# idx.fam = setdiff(1:length(g.fam.names), idx.pallete)
# tmp.palette <- colorRampPalette(c('#FFC26F', '#C38154', '#884A39', '#4E3636'))(length(idx.fam))
# idx.pallete = c(idx.pallete, idx.fam)
# fam.palette = c(fam.palette, tmp.palette)
#
# names(fam.palette) = g.fam.names[idx.pallete]
# fam.palette['Unassigned'] = 'grey'
# fam.palette['Mix'] = 'black'
# fam.palette['TEG'] = 'darkgreen'
Separately visualise connected components
tmp.graph <- igraph::make_graph(t(b.graph), directed = T)
tmp.graph <- igraph::simplify(tmp.graph)
tmp.comp <- igraph::components(tmp.graph)
tmp.cnt = table(tmp.comp$membership)
tmp.cnt = -sort(-tmp.cnt)
head(tmp.cnt)
2 51 209 176 104 29
3493 40 38 37 32 31 k = 1
tmp.k = as.numeric(names(tmp.cnt)[k])
tmp.names = names(tmp.comp$membership)[tmp.comp$membership == tmp.k]
b.graph.sub = b.graph[(b.graph[,1] %in% tmp.names) &
(b.graph[,2] %in% tmp.names),]
g.part.sub.big <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names.sub.big = network.vertex.names(g.part.sub.big)
set.seed(20)
p <- ggnet2(g.part.sub.big, label = F, edge.color = "black",
node.size = g.nodes.cnt[b.graph.names.sub.big],
color = g.nodes.fam[b.graph.names.sub.big],
mode = 'kamadakawai',
palette = fam.palette) + guides(size = F)Warning: 'length(x) = 3493 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3493 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3493 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3493 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3493 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3493 > 1' in coercion to 'logical(1)'p.big.type = p + theme(legend.position = "none")
# set.seed(20)
# p <- ggnet2(g.part.sub.big, label = F, edge.color = "black",
# node.size = g.nodes.cnt[b.graph.names.sub.big],
# color = g.nodes.fam[b.graph.names.sub.big],
# mode = 'kamadakawai',
# palette = fam.palette) + guides(size = F)
# p.big.color = p + theme(legend.position = "none")
tmp.k = as.numeric(names(tmp.cnt)[k])
tmp.names = names(tmp.comp$membership)[tmp.comp$membership != tmp.k]
b.graph.sub = b.graph[(b.graph[,1] %in% tmp.names) &
(b.graph[,2] %in% tmp.names),]
g.part.sub.small <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names.sub.small = network.vertex.names(g.part.sub.small)
set.seed(20)
p <- ggnet2(g.part.sub.small, label = F, edge.color = "black",
node.size = g.nodes.cnt[b.graph.names.sub.small],
color = g.nodes.fam[b.graph.names.sub.small],
# mode = 'kamadakawai',
palette = fam.palette) + guides(size = F)Warning: 'length(x) = 3416 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3416 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3416 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3416 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3416 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3416 > 1' in coercion to 'logical(1)'p.small.type =p + theme(legend.position = "none")
# set.seed(20)
# p <- ggnet2(g.part.sub.small, label = F, edge.color = "black",
# node.size = g.nodes.cnt[b.graph.names.sub.small],
# color = g.nodes.fam[b.graph.names.sub.small],
# # mode = 'kamadakawai',
# palette = fam.palette) + guides(size = F)
# p.small.color = p + theme(legend.position = "none")
Plots
p.big.type
p.small.type
pdf(paste(path.figures, 'graph_tes_family_small.pdf', sep = ''), width = 9, height = 9)print(p.small.type) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2 pdf(paste(path.figures, 'graph_tes_family_big.pdf', sep = ''), width = 5, height = 5)
print(p.big.type) # Plot 1 --> in the first page of PDFdev.off()quartz_off_screen
2
Stop for the paper
stop()Specific TE families
Graph of one family
sort(-table(df.query$subfam[(df.query$val.hits == 3) & (df.query$family == 'LTR/Copia')]))
META1 ATCOPIA95 ATCOPIA57 ATCOPIA28 ATCOPIA41 ATCOPIA49 ROMANIAT5 ATCOPIA94 ATCOPIA13 ATCOPIA37
-58 -51 -34 -33 -28 -28 -28 -27 -23 -22
ATCOPIA65 ATCOPIA43 ATCOPIA35 ATCOPIA42 ATCOPIA69 ATCOPIA78 ATCOPIA27 ATCOPIA58 ATRE1 ATCOPIA29
-22 -21 -16 -15 -15 -15 -14 -14 -14 -13
ATCOPIA54 ATCOPIA45 ATCOPIA51 ATCOPIA66 ATCOPIA75 ATCOPIA12 ATCOPIA36 ATCOPIA50 ATCOPIA67 ENDOVIR1
-12 -11 -11 -11 -11 -10 -10 -10 -10 -10
ATCOPIA16 ATCOPIA21 ATCOPIA22 ATCOPIA34 ATCOPIA4 ATCOPIA44 ATCOPIA48 ATCOPIA62 ATCOPIA63 ATCOPIA64
-9 -9 -9 -9 -9 -9 -9 -9 -9 -9
ATCOPIA87 ATCOPIA11 ATCOPIA55 ATCOPIA61 ATCOPIA70 ATCOPIA15 ATCOPIA25 ATCOPIA30 ATCOPIA52 ATCOPIA93
-9 -8 -8 -8 -8 -7 -7 -7 -7 -7
ATCOPIA96 ATCOPIA26 ATCOPIA31 ATCOPIA56 ATCOPIA8A ATCOPIA9 ATCOPIA1 ATCOPIA10 ATCOPIA23 ATCOPIA3
-7 -6 -6 -6 -6 -6 -5 -5 -5 -5
ATCOPIA31A ATCOPIA38 ATCOPIA40 ATCOPIA5 ATCOPIA83 ATCOPIA89 ATCOPIA91 ATCOPIA97 ATCOPIA14 ATCOPIA17
-5 -5 -5 -5 -5 -5 -5 -5 -4 -4
ATCOPIA2 ATCOPIA24 ATCOPIA32 ATCOPIA33 ATCOPIA38B ATCOPIA39 ATCOPIA46 ATCOPIA68 ATCOPIA74 ATCOPIA88
-4 -4 -4 -4 -4 -4 -4 -4 -4 -4
ATCOPIA8B ATCOPIA90 ATCOPIA19 ATCOPIA60 ATCOPIA72 ATCOPIA76 ATCOPIA77 ATCOPIA79 ATCOPIA82 ATCOPIA85
-4 -4 -3 -3 -3 -3 -3 -3 -3 -3
ATCOPIA86 TA1-2 ATCOPIA18 ATCOPIA18A ATCOPIA20 ATCOPIA32B ATCOPIA47 ATCOPIA53 ATCOPIA59 ATCOPIA65A
-3 -3 -2 -2 -2 -2 -2 -2 -2 -2
ATCOPIA71 ATCOPIA73 ATCOPIA81 ATCOPIA92 ATCOPIA38A ATCOPIA6 ATCOPIA7 ATCOPIA80 ATCOPIA84
-2 -2 -2 -2 -1 -1 -1 -1 -1
# one.te.fam = 'BRODYAGA1'
# one.te.fam = 'BRODYAGA2'
# one.te.fam = 'HELITRONY1D'
# one.te.fam = 'HELITRONY3'
one.te.fam = 'ATCOPIA41'
query.fam = df.query$query[df.query$subfam == one.te.fam]
one.te.fam = 'ATCOPIA41'
query.fam = df.query$query[df.query$subfam == one.te.fam]
res.nest.famp = res.nest[(res.nest$V1 %in% query.fam) | (res.nest$V8 %in% query.fam),]
idx = res.nest.famp$p1 >= sim.cutoff
edges = cbind(res.nest.famp$V1[idx], res.nest.famp$V8[idx])
idx = res.nest.famp$p8 >= sim.cutoff
edges = rbind(edges, cbind(res.nest.famp$V8[idx], res.nest.famp$V1[idx]))
te.enges.names = unique(c(edges[,1], edges[,2]))
te.enges.fam = sapply(te.enges.names, function(s) strsplit(s, '\\|')[[1]][9] )
te.enges.fam[te.enges.fam %in% c('DNA/Pogo', 'DNA/Tc1', 'DNA/Harbinger', 'DNA/En-Spm',
'DNA/HAT', 'DNA', 'DNA/Mariner')] = 'DNA'
te.enges.fam[te.enges.fam %in% c('RathE1_cons', 'RathE2_cons', 'RathE3_cons')] = 'RathE1/2/3_cons'
te.enges.fam[te.enges.fam %in% c('LINE/L1', 'LINE?')] = 'LINE'
te.enges.fam[te.enges.fam %in% c('Unassigned')] = 'Mix'
te.enges.fam[te.enges.fam %in% c('RC/Helitron')] = 'Helitron'
g.part <- network(edges, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)
b.graph.len = as.numeric(sapply(strsplit(b.graph.names, "\\|"), function(x) x[5]))
label.family = sapply(strsplit(b.graph.names, "\\|"), function(x) x[8])
lab.cols = c('#3F2E3E', "white")
label.color = lab.cols[(label.family == one.te.fam) + 1]
set.seed(20)
p <- ggnet2(g.part, label = b.graph.len, edge.color = "black",
node.size = 15,
alpha=0.8,
arrow.gap = 0.015,
arrow.size = 5,
label.color = label.color,
# node.size = g.nodes.cnt[b.graph.names],
color = te.enges.fam[b.graph.names],
palette = fam.palette,
# mode = "kamadakawai"
) + guides(size = F)
p
pdf(paste(path.figures, 'real_tes_subfam_', one.te.fam, '.pdf', sep = ''), width = 20, height = 18)
print(p) # Plot 1 --> in the first page of PDF
dev.off()
set.seed(20)
p <- ggnet2(g.part, label = b.graph.names, edge.color = "black",
node.size = 15,
alpha=0.8,
arrow.gap = 0.015,
arrow.size = 5,
# label.color = label.color,
# node.size = g.nodes.cnt[b.graph.names],
color = te.enges.fam[b.graph.names],
palette = fam.palette,
# mode = "kamadakawai"
) + guides(size = F)
pdf(paste(path.figures, 'real_tes_subfam_', one.te.fam, '_names.pdf', sep = ''), width = 50, height = 49)
print(p) # Plot 1 --> in the first page of PDF
dev.off()
Dotplots
Read TE sequences
file.te.fasta = '/Volumes/Samsung_T5/vienn/tair/new_filtration/new_te.fasta'
te.fasta = seqinr::read.fasta(file.te.fasta)Warning: cannot open file '/Volumes/Samsung_T5/vienn/tair/new_filtration/new_te.fasta': No such file or directoryError in file(con, "r") : cannot open the connectionOne pairwise example
one VS all
wsize = 10
nmatch = 8
name0 = 'te|11683565|11689821|3|6257|+|AT3TE48540|ATCOPIA95|LTR/Copia'
name0 = 'te|16691748|16695154|1|3407|−|AT1TE55070|ATCOPIA41|LTR/Copia'
name0 = gsub('−', "-", name0)
one.te.fam = strsplit(name0, '\\|')[[1]][8]
# one.te.fam = 'BRODYAGA2'
query.fam = df.query$query[df.query$subfam == one.te.fam]
query.fam = query.fam[(query.fam %in% res.nest.sim$V1) | (query.fam %in% res.nest.sim$V2)]
names.all = setdiff(query.fam, name0)
p.all = list()
for(name2 in names.all){
# message(name2)
seq1 = te.fasta[[name0]]
seq2 = te.fasta[[name2]]
s1 = strsplit(name0, '\\|')[[1]][7]
s2 = strsplit(name2, '\\|')[[1]][7]
p = dotplot(seq1, seq2, wsize, nmatch) + xlab(s1) + ylab(s2)
p.all[[name2]] = p
}
#
# pp = grid.arrange(grobs = p.all, ncol = 13) ## display plot
#
#
# pdf(paste(path.figures, 'pairwise_all','.pdf', sep = ''), width = 50, height = 50)
# print(pp) # Plot 1 --> in the first page of PDF
# dev.off()
s0 = paste0(strsplit(name0, '\\|')[[1]][7:9], collapse = '_')
s0 = gsub("/", "-", s0)
pdf(paste(path.figures, 'pairwise_all_',s0,'.pdf', sep = ''), width = 50, height = 50)
grid.arrange(grobs = p.all, ncol = ceiling(sqrt(length(p.all)))) # Write the grid.arrange in the file
dev.off() # Close the file
one connected component
wsize = 10
nmatch = 8
name0 = 'te|6205621|6206184|2|564|−|AT2TE25255|HELITRONY1D|RC/Helitron'
name0 = 'te|14189256|14190266|5|1011|-|AT5TE50700|HELITRONY3|RC/Helitron'
name0 = 'te|12513239|12513824|1|586|+|AT1TE40725|ATHILA4A|LTR/Gypsy'
name0 = 'te|11647426|11648912|1|1487|+|AT1TE37705|ATREP7|RC/Helitron'
name0 = 'te|11683565|11689821|3|6257|+|AT3TE48540|ATCOPIA95|LTR/Copia'
name0 = gsub('−', "-", name0)
names.all = unique(c(res.nest.sim$V1[res.nest.sim$V8 == name0],
res.nest.sim$V8[res.nest.sim$V1 == name0]))
# names.all = unique(c(res.nest$V1[res.nest$V8 == name0],
# res.nest$V8[res.nest$V1 == name0]))
p.all = list()
for(name2 in names.all){
# message(name2)
seq1 = te.fasta[[name0]]
seq2 = te.fasta[[name2]]
s1 = paste0(strsplit(name0, '\\|')[[1]][7:9], collapse = '|')
s2 = paste0(strsplit(name2, '\\|')[[1]][7:9], collapse = '|')
p = dotplot(seq1, seq2, wsize, nmatch) + xlab(s1) + ylab(s2)
p.all[[name2]] = p
}
s0 = paste0(strsplit(name0, '\\|')[[1]][7:9], collapse = '_')
s0 = gsub("/", "-", s0)
pdf(paste(path.figures, 'pairwise_connect_',s0,'.pdf', sep = ''), width = 50, height = 50)
grid.arrange(grobs = p.all, ncol = ceiling(sqrt(length(p.all)))) # Write the grid.arrange in the file
dev.off() # Close the file
name1 = 'te|14189256|14190266|5|1011|-|AT5TE50700|HELITRONY3|RC/Helitron'
name2 = 'te|2162295|2162937|2|643|-|AT2TE09950|HELITRONY3|RC/Helitron'
name0 = name1
names.all = unique(c(res.nest$V1[res.nest$V8 == name0], res.nest$V8[res.nest$V1 == name0]))
names = c(name1, name2)
b.tmp = bl.res[(bl.res$V1 %in% names) & (bl.res$V8 %in% names),]
res.nest[(res.nest$V1 %in% names) & (res.nest$V8 %in% names), ]
SVs
Readings seSVs
sv.se = readRDS(paste(path.svs, 'sv_se.rds', sep = ''))
# Rename length groups
lev.replace = c('[1,10]', '(10,15]')
lev.new = '[1,15]'
s.levels = as.character(levels(sv.se$len.gr))
s.levels = s.levels[!(s.levels %in% lev.replace)]
s.levels = c(lev.new, s.levels)
s.levels = gsub("e\\+03", "k", s.levels)
sv.se$len.gr = as.character(sv.se$len.gr)
sv.se$len.gr[sv.se$len.gr %in% lev.replace] = lev.new
sv.se$len.gr = gsub("e\\+03", "k", sv.se$len.gr)
sv.se$len.gr = factor(sv.se$len.gr, levels = s.levels)
# Replace families
sv.se$fam = as.character(sv.se$fam)
sv.se$fam <- gsub("Helitron/.*", "Mix with Helitron", sv.se$fam)
sv.se$te = factor(sv.se$te, levels = c('isTE', 'isTEpart', 'hasTE', 'hasTEpart', 'noTE'))
Read SV fasta
sv.seqs = seqinr::read.fasta(paste(path.svs, 'sv_pangen_seq_sv_big.fasta', sep = ''))
Reading nestedness
# Load similarity function
file.nestedness = paste(path.work, 'sv_big_on_big_nest.rds', sep = '')
if(!file.exists(file.nestedness)){
bl.file = paste(path.work, 'sv_big_on_big.txt', sep = '')
bl.res = read.table(bl.file)
bl.res = bl.res[bl.res$V1 != bl.res$V8,]
bl.res = bl.res[bl.res$V6 >= sim.cutoff * 100,]
res.nest = findNestedness(bl.res, use.strand = F)
res.nest$len1 = res.nest.len[res.nest$V1]
res.nest$len8 = res.nest.len[res.nest$V8]
res.nest$p1 = res.nest$C1 / res.nest$len1
res.nest$p8 = res.nest$C8 / res.nest$len8
saveRDS(res.nest, file.nestedness, compress = F)
} else {
res.nest = readRDS(file.nestedness)
}
res.nest.len = sapply(unique(c(res.nest$V1, res.nest$V8)),
function(s) as.numeric(strsplit(s, '\\|')[[1]][2]))
res.nest0 = res.nest
# For further analysis of examples:
bl.file = paste(path.work, 'sv_big_on_big.txt', sep = '')
bl.res = read.table(bl.file)
bl.res = bl.res[bl.res$V1 != bl.res$V8,]
TE stat
In graph - not in graph
res.nest = res.nest0
sv.se.len = sv.se[sv.se$len >= 100,]
sv.se.len$in.connect = sv.se.len$name %in% names(res.nest.len)
cnt.sv.se = table(sv.se.len$in.connect , sv.se.len$te)
cnt.sv.se
isTE isTEpart hasTE hasTEpart noTE
FALSE 80 749 254 538 5748
TRUE 4260 2560 2830 1384 1916df = reshape2::melt(cnt.sv.se)
df$Var2 = factor(df$Var2, levels = rev(c('isTE', 'isTEpart', 'hasTE', 'hasTEpart', 'noTE')))
# install.packages("ggpattern")
p = ggplot(df, aes(x = Var2, y = value, fill = Var2, alpha = Var1, color = Var1)) +
geom_col_pattern( aes(pattern = Var1),
# pattern = rep(c('none', "stripe"), 5),
pattern_density = 0.1,
pattern_spacing = 0.025,
pattern_fill = "grey70",
position = "dodge",
width = 0.8
) +
# geom_col(position = "dodge", width = 0.8) +
scale_alpha_manual(values = c(0.8, 1), labels = c("No", "Yes")) +
scale_color_manual(values = c('black', 'black'), labels = c("not in graph", "in graph")) +
scale_pattern_manual(values = c("stripe", 'none'), labels = c("in graph", "not in graph"),
breaks = c(TRUE, FALSE)) +
labs(fill = "", pattern='Connected to others') +
scale_fill_manual(values = te.cols) +
xlab(NULL) +
ylab("Number of SVs") +
theme(axis.text.y = element_blank()) +
guides(alpha = "none", fill = 'none', color = 'none') +
theme_minimal() + coord_flip() +
theme(
legend.position = c(0.7, 0.3), # Adjust these coordinates as needed
legend.background = element_rect(fill="transparent", color='grey70')
) +
theme(axis.text.y = element_blank()) +
guides(pattern = guide_legend(override.aes = list(fill = c("white"), color= 'black')))
p
pdf(paste(path.figures, 'graph_mob_in_graph.pdf', sep = ''), width = 3, height = 4)print(p) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2 
TE families in SV types
sv.se.len = sv.se[sv.se$len >= 100,]
cnt.fam.sv = table(sv.se.len$fam[sv.se.len$fam!=''], sv.se.len$te[sv.se.len$fam!=''])
cnt.fam.sv = t(apply(cnt.fam.sv, 1, function(x) x/sum(x)))
cnt.fam.sv = cnt.fam.sv[, colSums(cnt.fam.sv) != 0]
cnt.fam.sv = reshape2::melt(cnt.fam.sv)
p = ggplot(cnt.fam.sv, aes(x = Var2, y = Var1, color = Var2)) +
geom_point(aes(size = value, alpha = value * 2)) + theme_minimal() +
scale_color_manual(values = te.cols) +
geom_text(data = cnt.fam.sv[cnt.fam.sv$value >= 0.2,],
aes(x=Var2, y=Var1, label = round(value, 2)),
size = 2.5, color = 'black',
nudge_x = 0.3,
nudge_y = 0) +
guides(size = "none", alpha = "none", color = 'none') +
xlab('SV type') + ylab('TE family')
p
cnt.fam.sv = rowSums(table(sv.se.len$fam[sv.se.len$fam!=''], sv.se.len$te[sv.se.len$fam!='']))
cnt.fam.sv = data.frame(value = cnt.fam.sv, names = names(cnt.fam.sv))
rownames(cnt.fam.sv) = NULL
g = ggplot(cnt.fam.sv, aes(x = names, y = value)) +
geom_bar(stat="identity", fill="grey80")+
coord_flip() + theme_minimal() + theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()) +
scale_y_continuous(labels = paste("1e",seq(0,4,1), sep = ''), breaks= seq(0,4,1)*1000) +
ylab('#') + geom_text(aes(label=value, y=0), hjust=0, size = 2.5)
g 
pp = ggpubr::ggarrange(p + xlab('TE content') + scale_x_discrete(labels = c('is compl.', 'is fragm.',
'cont. compl.', 'cont. fragm.')) , g, ncol = 2, widths = c(0.75, 0.25))
pp
pdf(paste(path.figures, 'graph_mob_te_fam_sv_type.pdf', sep = ''), width = 6, height = 4)print(pp) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2 
# Insertion and deletion
idx = (sv.se.len$fam!='') & (sv.se.len$freq.max <= 3)
table(sv.se.len$fam[idx], sv.se.len$te[idx])
isTE isTEpart hasTE hasTEpart noTE
DNA/HAT 123 20 91 35 0
DNA/MuDR 529 195 494 114 0
DNA+ 323 78 263 57 0
Helitron 1192 651 395 354 0
LINE 66 162 257 129 0
LTR/Copia 753 180 216 201 0
LTR/Gypsy 121 170 150 62 0
Mix 0 16 152 87 0
Mix with Helitron 104 22 172 68 0
RathE1/2/3_cons 8 12 8 9 0
SINE 1 0 3 2 0
TEG 72 95 23 72 0
Unassigned 12 20 21 33 0idx = (sv.se.len$fam!='') & (sv.se.len$freq.max >= 25) & (sv.se.len$len >= 100)
table(sv.se.len$fam[idx], sv.se.len$te[idx])
isTE isTEpart hasTE hasTEpart noTE
DNA/HAT 7 5 5 3 0
DNA/MuDR 10 111 31 20 0
DNA+ 10 55 25 13 0
Helitron 18 241 64 111 0
LINE 8 39 14 20 0
LTR/Copia 5 49 16 7 0
LTR/Gypsy 2 121 22 13 0
Mix 0 14 7 10 0
Mix with Helitron 0 13 7 13 0
RathE1/2/3_cons 1 1 7 2 0
SINE 1 1 2 3 0
TEG 2 36 2 12 0
Unassigned 1 6 0 1 0TE fam: TAIR10
f.te.ref = paste(path.tair, 'new_te.fasta', sep = '')
lines = readLines(f.te.ref)
lines = grep('^>', lines, value = T)
ref.fam = sapply(lines, function(x) strsplit(x, '\\|')[[1]][9])
indices <- grep("^DNA(?!/HAT|/MuDR)", ref.fam, value = FALSE, perl = TRUE)
ref.fam[indices] = 'DNA+'
indices <- grep("^RathE", ref.fam, value = FALSE, perl = TRUE)
ref.fam[indices] = 'RathE1/2/3_cons'
indices <- grep("^LINE", ref.fam, value = FALSE, perl = TRUE)
ref.fam[indices] = 'LINE'
ref.fam[ref.fam == 'RC/Helitron'] = 'Helitron'
ref.fam.cnt = table(ref.fam)
df = cnt.fam.sv
df$ref = as.numeric(ref.fam.cnt[df$names])
df = df[!is.na(df$ref),]
plot(df$value, df$ref)
p <- ggplot(df, aes(x = ref, y = value, color = names)) +
geom_smooth(aes(group = 1), method = "lm", formula = y ~ x, se = FALSE, color = 'grey70') +
geom_point() +
ggrepel::geom_text_repel(aes(label = names), max.overlaps = 20) +
# xlab("log # in TAIR10 annotation") +
# ylab("log # in SVs") +
# scale_x_log10() +
# scale_y_log10() +
xlab("# in TAIR10 annotation") +
ylab("# in SVs") +
theme(legend.position = "none") +
guides(color = FALSE) +
theme_minimal()Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as of ggplot2 3.3.4.p
lm_model <- lm(value ~ ref, data = df)
slope <- coef(lm_model)[2]
p = p + annotate("text", x = min(df$ref), y = max(df$value),
label = paste('Slope:', round(slope, 3)), hjust = 0, vjust = 1)
pdf(paste(path.figures, 'graph_mob_te_fam_tair10.pdf', sep = ''), width = 4, height = 4)print(p) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2
Graph
Filtration
res.nest = res.nest0
sv.names.mix = sv.se$name[grep("^Mix", sv.se$fam)]
res.nest = res.nest[!(res.nest$V1 %in% sv.names.mix),]
res.nest = res.nest[!(res.nest$V8 %in% sv.names.mix),]
sv.names.mix = sv.se$name[sv.se$te == 'noTE']
res.nest = res.nest[!(res.nest$V1 %in% sv.names.mix),]
res.nest = res.nest[!(res.nest$V8 %in% sv.names.mix),]
singleton.mode = F
if(singleton.mode){
sv.names.freq = sv.se$name[sv.se$freq.max <= 3]
# sv.names.freq = sv.se$name[sv.se$freq.max >= 25]
res.nest = res.nest[res.nest$V1 %in% sv.names.freq,]
res.nest = res.nest[res.nest$V8 %in% sv.names.freq,]
}
prefix.mode = c('', '_single')
Construct
# all edges
idx = res.nest$p1 >= sim.cutoff
edges = cbind(res.nest$V1[idx], res.nest$V8[idx])
idx = res.nest$p8 >= sim.cutoff
edges = rbind(edges, cbind(res.nest$V8[idx], res.nest$V1[idx]))
te.enges.names = unique(c(edges[,1], edges[,2]))
tmp = sv.se$te
names(tmp) = sv.se$name
te.enges.type = as.character(tmp[te.enges.names])
names(te.enges.type) <- names(tmp[te.enges.names])
tmp = sv.se$fam
names(tmp) = sv.se$name
te.enges.fam = tmp[te.enges.names]
# nodes
idx = (res.nest$p1 >= sim.cutoff) & (res.nest$p8 >= sim.cutoff)
te.nodes = cbind(res.nest$V1[idx], res.nest$V8[idx])
te.rest = setdiff(te.enges.names, c(te.nodes[,1], te.nodes[,2]))
te.nodes.graph <- igraph::make_graph(t(te.nodes), directed = T)
te.nodes.graph <- igraph::simplify(te.nodes.graph)
te.nodes.comp <- igraph::components(te.nodes.graph)
nodes = data.frame(node = paste('N', te.nodes.comp$membership, sep = ''), te = names(te.nodes.comp$membership))
nodes.rest = data.frame(node = paste('R', (1:length(te.rest)), sep = ''), te = te.rest)
nodes = rbind(nodes, nodes.rest)
rownames(nodes) = nodes$te
# Define TE type
nodes.cnt = data.frame(cnt = c(table(nodes$node)))
nodes.cnt$node = rownames(nodes.cnt)
nodes.cnt$type = sapply(nodes.cnt$node, function(s){
s.te = nodes$te[nodes$node == s]
type.te = unique(te.enges.type[s.te])
if(length(type.te) == 1){
return(type.te)
} else {
type.te = table(type.te)
type.te = names(type.te)[type.te == max(type.te)]
return(type.te[1])
}
})
table(nodes.cnt$type)
hasTE hasTEpart isTE isTEpart
1027 447 484 1846 # Define TE family
nodes.cnt$fam = sapply(nodes.cnt$node, function(s){
s.te = nodes$te[nodes$node == s]
type.te = unique(te.enges.fam[s.te])
if(length(type.te) == 1){
return(type.te)
} else {
type.te = table(type.te)
type.te = names(type.te)[type.te == max(type.te)]
return(type.te[1])
}
})
table(nodes.cnt$fam)
DNA/HAT DNA/MuDR DNA+ Helitron LINE LTR/Copia LTR/Gypsy
123 688 359 807 444 456 684
RathE1/2/3_cons SINE TEG Unassigned
28 13 151 51 # Redefine edges but with node names
idx.endes = (edges[,1] %in% nodes$te) & (edges[,2] %in% nodes$te)
b.graph = cbind(nodes[edges[idx.endes,1], 'node'],nodes[edges[idx.endes,2], 'node'])
b.graph = unique(b.graph)
# b.graph = b.graph[b.graph[,1] != b.graph[,2],]
b.graph.uni = b.graph[b.graph[,1] == b.graph[,2],]
b.graph = b.graph[b.graph[,1] != b.graph[,2],]
length(unique(c(b.graph[,1], b.graph[,2])))[1] 3567# reduce indirect arrows
idx.remove = c()
for(i.edge in 1:nrow(b.graph)){
if(i.edge %% 1000 == 0) print(i.edge)
tmp.to = b.graph[b.graph[,1] == b.graph[i.edge,1],2]
tmp.from = b.graph[b.graph[,2] == b.graph[i.edge,2],1]
if(length(intersect(tmp.to, tmp.from)) > 0) idx.remove = c(idx.remove, i.edge)
}[1] 1000
[1] 2000
[1] 3000
[1] 4000
[1] 5000
[1] 6000
[1] 7000
[1] 8000idx.remove = unique(idx.remove)
b.graph = b.graph[-idx.remove,]
# b.graph = rbind(b.graph, b.graph.uni)
# Print graph
g.nodes.type = nodes.cnt$type
names(g.nodes.type) = nodes.cnt$node
g.nodes.cnt = nodes.cnt$cnt
names(g.nodes.cnt) = nodes.cnt$node
g.nodes.fam = nodes.cnt$fam
names(g.nodes.fam) = nodes.cnt$node
# g.cols.names = c("noTE", "isTE", "hasTE", "hasTEpart", "isTEpart")
# g.cols = c('#FFD966', '#EB455F', '#7B6079', '#3C8DAD', '#79B773')
# names(g.cols) = g.cols.names
g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)
set.seed(20)
p <- ggnet2(g.part, label = F, edge.color = "black",
node.size = g.nodes.cnt[b.graph.names],
color = g.nodes.type[b.graph.names],
# mode = 'kamadakawai',
# arrow.gap = 0,
# arrow.size = 3,
palette = te.cols) + guides(size = F)Loading required package: sna
Loading required package: statnet.common
Attaching package: ‘statnet.common’
The following object is masked from ‘package:Biostrings’:
order
The following object is masked from ‘package:XVector’:
order
The following object is masked from ‘package:IRanges’:
order
The following object is masked from ‘package:S4Vectors’:
order
The following object is masked from ‘package:BiocGenerics’:
order
The following objects are masked from ‘package:base’:
attr, order
sna: Tools for Social Network Analysis
Version 2.7-1 created on 2023-01-24.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
For citation information, type citation("sna").
Type help(package="sna") to get started.
Attaching package: ‘sna’
The following objects are masked from ‘package:igraph’:
betweenness, bonpow, closeness, components, degree, dyad.census, evcent, hierarchy, is.connected,
neighborhood, triad.census
Loading required package: scales
Attaching package: ‘scales’
The following object is masked from ‘package:viridis’:
viridis_pal
Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as of ggplot2 3.3.4.p
# path.figures = '/Volumes/Samsung_T5/vienn/work_te/'
pdf(paste(path.figures, 'graph_mob_all_cluster', prefix.mode[singleton.mode+1] ,'_type.pdf', sep = ''),
width = 4.6, height = 4.6)print(p+ theme(legend.position = "none")) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2 
# set.seed(20)
# p <- ggnet2(g.part, label = F, edge.color = "grey30",
# node.size = g.nodes.cnt[b.graph.names],
# color = c('TE', 'noTE')[(g.nodes.type[b.graph.names] == 'noTE')*1+1],
# # mode = 'kamadakawai',
# # arrow.gap = 0,
# # arrow.size = 3,
# palette = c('noTE' = 'black', 'TE' = '#AEC3AE')) + guides(size = F)
# p
#
# # path.figures = '/Volumes/Samsung_T5/vienn/work_te/'
# pdf(paste(path.figures, 'graph_mob_all_cluster', prefix.mode[singleton.mode+1] ,'_type.pdf', sep = ''),
# width = 5, height = 5)
# print(p+ theme(legend.position = "none")) # Plot 1 --> in the first page of PDF
# dev.off()
Colored by TE family
if(length(setdiff(g.nodes.fam, names(fam.palette)))!=0) stop('not all families are defined')
set.seed(20)
p <- ggnet2(g.part, label = F, edge.color = "grey20",
node.size = g.nodes.cnt[b.graph.names],
color = g.nodes.fam[b.graph.names],
# mode = 'kamadakawai',
# arrow.gap = 0,
# arrow.size = 3,
palette = fam.palette) + guides(size = F)Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 3565 > 1' in coercion to 'logical(1)'p = p + theme(legend.text = element_text(size = 8),
legend.title = element_blank(),
legend.key.size = unit(0.5, "cm")) + guides(color = guide_legend(ncol = 2))
p
pdf(paste(path.figures, 'graph_mob_all_cluster', prefix.mode[singleton.mode+1] ,'_family.pdf', sep = ''),
width = 4.6, height = 4.6)print(p+ theme(legend.position = "none")) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2 pdf(paste(path.figures, 'graph_mob_all_cluster', prefix.mode[singleton.mode+1] ,'_family_legend.pdf', sep = ''), width = 7, height = 5)
print(p+ coord_fixed(ratio = 1)) # Plot 1 --> in the first page of PDFdev.off()quartz_off_screen
2
Node size distribution
df = data.frame(node = unique(nodes$node))
df$size = g.nodes.cnt[df$node]
df$fam = g.nodes.fam[df$node]
df$type = g.nodes.type[df$node]
fam.palette Unassigned Mix Mix with Helitron Helitron LTR/Copia LTR/Gypsy
"grey" "grey20" "#266D98" "#BCACDE" "#BFDB38" "#54B435"
DNA/HAT DNA+ DNA DNA/MuDR LINE RathE1/2/3_cons
"#F9B5D0" "#C8658C" "#C8658C" "#971549" "#FFC26F" "#C38154"
SINE TEG
"#884A39" "#4E3636" p = ggplot(df, aes(x = type, y = size, color=fam)) +
geom_jitter(width = 0.2) +
labs(x = "Type", y = "Size") +
scale_y_continuous(trans = "log2") +
scale_color_manual(values = fam.palette)+
theme_minimal() +
guides(color = guide_legend(ncol = 2)) +
labs(color = "TE family") + xlab('') + ylab('Node size (Number of similar SVs)')
p
pdf(paste(path.figures, 'graph_mob_size_distribution.pdf', sep = ''), width = 6.5, height = 4)print(p) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2
Separately visualise connected components
tmp.graph <- igraph::make_graph(t(b.graph), directed = T)
tmp.graph <- igraph::simplify(tmp.graph)
tmp.comp <- igraph::components(tmp.graph)
tmp.cnt = table(tmp.comp$membership)
tmp.cnt = -sort(-tmp.cnt)
head(tmp.cnt)
1 39 4 26 34 33
1574 41 36 28 24 23 Save
# p.big.type
# p.big.color
# p.small.type
# p.small.color
size.units = 4.6
pdf(paste(path.figures, 'graph_mob_big_cluster', prefix.mode[singleton.mode+1] ,'_type.pdf', sep = ''),
width = size.units, height = size.units)
print(p.big.type) # Plot 1 --> in the first page of PDF
dev.off()null device
1 pdf(paste(path.figures, 'graph_mob_big_cluster', prefix.mode[singleton.mode+1] ,'_family.pdf', sep = ''),
width = size.units, height = size.units)print(p.big.color) # Plot 1 --> in the first page of PDF
dev.off()null device
1 pdf(paste(path.figures, 'graph_mob_small_cluster', prefix.mode[singleton.mode+1] ,'_type.pdf', sep = ''),
width = 6, height = 6)print(p.small.type) # Plot 1 --> in the first page of PDF
dev.off()null device
1 pdf(paste(path.figures, 'graph_mob_small_cluster', prefix.mode[singleton.mode+1] ,'_family.pdf', sep = ''),
width = 6, height = 6)print(p.small.color) # Plot 1 --> in the first page of PDF
dev.off()null device
1 Examples with big “has TE”
path.components = paste(path.figures, 'components/', sep = '')
if (!dir.exists(path.components)) {
dir.create(path.components)
}
# Find a big node, whish is "Have te", which which is not in the biggest connected component
nodes.havete.big = nodes.cnt$node[(nodes.cnt$cnt >= 7) & ((nodes.cnt$type == 'hasTE') | (nodes.cnt$type == 'hasTEpart'))]
nodes.havete.big = nodes.havete.big[nodes.havete.big %in% names(tmp.comp$membership)]
nodes.target = unique(nodes.havete.big[tmp.comp$membership[nodes.havete.big] != as.numeric(names(tmp.cnt)[1])])
# for(i.target in 1:min(80, length(nodes.target))){
for(i.target in c(1)){
message(i.target)
comp.target = as.numeric(tmp.comp$membership[nodes.target[i.target]])
# Visualise one component
tmp.k = comp.target
tmp.names = names(tmp.comp$membership)[tmp.comp$membership == tmp.k]
b.graph.sub = b.graph[(b.graph[,1] %in% tmp.names) |
(b.graph[,2] %in% tmp.names),,drop=F]
g.part.sub.big <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names.sub.big = network.vertex.names(g.part.sub.big)
set.seed(20)
p.te <- ggnet2(g.part.sub.big, label = F, edge.color = "black",
node.size = g.nodes.cnt[b.graph.names.sub.big],
color = g.nodes.type[b.graph.names.sub.big],
mode = 'kamadakawai',
arrow.gap = 0.04, arrow.size = 5,
palette = te.cols) + guides(size = F)+ theme(legend.position = "none")
set.seed(20)
p.fam <- ggnet2(g.part.sub.big, label = g.nodes.cnt[b.graph.names.sub.big], edge.color = "black",
node.size = g.nodes.cnt[b.graph.names.sub.big],
# node.size = 10,
color = g.nodes.fam[b.graph.names.sub.big],
mode = 'kamadakawai',
arrow.gap = 0.04, arrow.size = 5,
palette = fam.palette) + guides(size = F)+ theme(legend.position = "none")
pp = ggarrange(p.te, p.fam, nrow=1)
# pp
# TODO: third plot - frequency plot (three of colors: insertion, deletion, indel)
pdf(paste(path.components, 'graph_mob_component_', sprintf("%03d", i.target),'.pdf', sep = ''),
width = 6, height = 3)
print(pp) # Plot 1 --> in the first page of PDF
dev.off()
}1
Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Warning: 'length(x) = 5 > 1' in coercion to 'logical(1)'Get sequences and align
path.mafft = paste(path.work, 'mafft/', sep = '')
if (!dir.exists(path.mafft)) {
dir.create(path.mafft)
}
sv.name.target = nodes$te[nodes$node %in% tmp.names]
seqs.target = sv.seqs[sv.name.target]
seqs.target.len = as.numeric(sapply(names(seqs.target), function(s) strsplit(s, '\\|')[[1]][2]))
seqs.target = seqs.target[order(-seqs.target.len)]
sv.name.target = names(seqs.target)
# Find orientations of sequences
bl.target = bl.res[(bl.res$V1 %in% sv.name.target) & (bl.res$V8 %in% sv.name.target),]
orientation.target = rep('.', length(seqs.target))
names(orientation.target) = sv.name.target
orientation.target[1] = '+'
# TODO: define the first orientation by the longest ORF
dir.seq = c('-', '+')
for(i in 1:(length(orientation.target) - 1)){
bl.tmp = bl.target[bl.target$V1 == sv.name.target[i],]
bl.tmp = bl.tmp[order(-bl.tmp$V7),]
bl.tmp = bl.tmp[!duplicated(bl.tmp$V8),]
bl.tmp$dir = bl.tmp$V5 > bl.tmp$V4
bl.tmp = bl.tmp[orientation.target[bl.tmp$V8] == '.',]
if(nrow(bl.tmp) == 0) next
if(orientation.target[i] == '+'){
orientation.target[bl.tmp$V8] = dir.seq[1 + (bl.tmp$dir) * 1]
}
if(orientation.target[i] == '-'){
orientation.target[bl.tmp$V8] = dir.seq[2 - (bl.tmp$dir) * 1]
}
if(sum(orientation.target == '.') == 0) break
}
#
for(i in which(orientation.target == '-')){
seqs.target[[i]] = rev(seqinr::comp(seqs.target[[i]]))
}
seqs.target.msa = unlist(lapply(seqs.target, function(s) paste0(s, collapse = '')))
seqs.target.msa <- DNAStringSet(seqs.target.msa)
# alignment <- msa(seqs.target.msa)
# Run the alignment
tmp.fasta = paste(path.mafft, 'tmp.fasta', sep = '')
aln.fasta = paste(path.mafft, 'aln.fasta', sep = '')
writeXStringSet(seqs.target.msa, filepath = tmp.fasta)
system(paste('mafft --op 5 --quiet --maxiterate 100 ', tmp.fasta, '>', aln.fasta, sep = ' '))
alignment = readDNAStringSet(aln.fasta)
seqs.mx = as.matrix(alignment)
msaplot(seqs.mx)
Alignment step-by step
# Align all sequences within each node
# sv.name.target = nodes$te[nodes$node %in% tmp.names]
aln.nodes = list()
seqs.all.cons = c()
for(s.node in tmp.names){
sv.name.node = nodes$te[nodes$node %in% s.node]
seqs.name.node = seqs.target.msa[sv.name.node]
if(length(sv.name.node) == 1){
seqs.mx = as.matrix(seqs.name.node)
aln.nodes[[s.node]] = seqs.mx
seqs.all.cons[s.node] = as.character(seqs.name.node)
next
}
tmp.fasta = paste(path.mafft, 'tmp.fasta', sep = '')
aln.fasta = paste(path.mafft, 'aln.fasta', sep = '')
writeXStringSet(seqs.name.node, filepath = tmp.fasta)
system(paste('mafft --op 5 --quiet --maxiterate 100 ', tmp.fasta, '>', aln.fasta, sep = ' '))
alignment = readDNAStringSet(aln.fasta)[sv.name.node]
seqs.mx = as.matrix(alignment)
# msaplot(seqs.mx)
# add consensus sequence
cons.prof = seqinr::consensus(seqs.mx, method = 'profile')
cons.prof = cons.prof[!(rownames(cons.prof) == '-'),]
max.indexes <- apply(cons.prof, 2, which.max)
cons.seq = rownames(cons.prof)[max.indexes]
# seqs.mx = rbind(seqs.mx, cons.seq)
aln.nodes[[s.node]] = seqs.mx
seqs.all.cons[s.node] = paste0(cons.seq, collapse = '')
}
# Combine all seqs for the alignment
# Align consensuses
seqs.all.cons <- DNAStringSet(seqs.all.cons)
# Run the alignment
tmp.fasta = paste(path.mafft, 'tmp.fasta', sep = '')
aln.fasta = paste(path.mafft, 'aln.fasta', sep = '')
writeXStringSet(seqs.all.cons, filepath = tmp.fasta)
system(paste('mafft --op 5 --quiet --maxiterate 100 ', tmp.fasta, '>', aln.fasta, sep = ' '))
alignment = readDNAStringSet(aln.fasta)
seqs.mx.cons = as.matrix(alignment)
msaplot(seqs.mx.cons)
# Combine consensuses back
seqs.mx.complete = c()
for(s.node in rownames(seqs.mx.cons)){
aln.tmp = aln.nodes[[s.node]]
mx.tmp = matrix('-', nrow = nrow(aln.tmp), ncol = ncol(seqs.mx.cons), dimnames = list(rownames(aln.tmp), NULL))
mx.tmp[, seqs.mx.cons[s.node,] != '-'] = aln.tmp
seqs.mx.complete = rbind(seqs.mx.complete, mx.tmp)
}
p.msa = msaplot(seqs.mx.complete[sv.name.target,])
# p.msa = msaplot(seqs.mx.complete)
p.msa
pdf(paste(path.components, 'graph_mob_component_', sprintf("%03d", i.target),'_msa.pdf', sep = ''),
width = 6, height = 4)print(p.msa) # Plot 1 --> in the first page of PDF
dev.off()quartz_off_screen
2 
Create graph from without node collapse
s = rownames(seqs.mx)
s1 = as.character(as.vector(seqs.target.msa[[1]]))
s2 = as.character(as.vector(seqs.target.msa[[4]]))
dotplot(s1, s2, 10, 9)
s1 = as.character(as.vector(seqs.target.msa[['SVgr_1_id_122936|638']]))
s2 = as.character(as.vector(seqs.target.msa[["SVgr_5_id_136083|674"]]))
dotplot(s1, s2, 10, 8)
bl.target[(bl.target$V1 %in% s.tmp) & (bl.target$V8 %in% s.tmp), ]
s1 = as.character(as.vector(seqs.target.msa[[1]]))
s2 = as.character(as.vector(seqs.target.msa[[4]]))
dotplot(s1, s2, 10, 8)
s2 = as.character(seqs.target.msa[[1]])
# todo: %ORFStop
stop('All graphs are created, Stop before the code for \"per accession\" ')Run by accessions
path.figures.acc = '/Volumes/Samsung_T5/vienn/work_te/figures_tegraph_accessions/'
sv.bin = read.table('/Volumes/Samsung_T5/vienn/work_sv/svs_se_bin_v03.txt', stringsAsFactors = F, check.names = FALSE)# acc = '10002'
for(acc in colnames(sv.bin)){
sv.acc = rownames(sv.bin)[sv.bin[,acc] == 1]
rownames(sv.se) = sv.se$gr
sv.acc = sv.se[sv.acc, 'name']
sv.acc = intersect(sv.acc, rownames(nodes))
nodes.cnt.acc = table(nodes[sv.acc,'node'])
sv.alpha = rep(0, length(b.graph.names))
names(sv.alpha) = b.graph.names
sv.alpha[names(sv.alpha) %in% names(nodes.cnt.acc)] = 1
# set.seed(239)
# p <- ggnet2(g.part, label = F, edge.color = "black",
# node.size = g.nodes.cnt[b.graph.names],
# color = g.nodes.fam[b.graph.names],
# alpha = sv.alpha,
# # mode = 'kamadakawai',
# # arrow.gap = 0,
# # arrow.size = 3,
# palette = fam.palette) + guides(size = F) + theme(legend.position = "none")
set.seed(20)
p <- ggnet2(g.part.sub.small, label = F, edge.color = "black",
node.size = g.nodes.cnt[b.graph.names.sub.small],
color = g.nodes.fam[b.graph.names.sub.small],
alpha = sv.alpha[b.graph.names.sub.small],
# mode = 'kamadakawai',
palette = fam.palette) + guides(size = F) + theme(legend.position = "none")
pdf(paste(path.figures.acc, 'graph_te', prefix.mode[singleton.mode+1] ,'_small_acc_',acc,'.pdf', sep = ''), width = 5, height = 5)
print(p) # Plot 1 --> in the first page of PDF
dev.off()
set.seed(20)
p <- ggnet2(g.part.sub.big, label = F, edge.color = "black",
node.size = g.nodes.cnt[b.graph.names.sub.big],
color = g.nodes.fam[b.graph.names.sub.big],
alpha = sv.alpha[b.graph.names.sub.big],
mode = 'kamadakawai',
palette = fam.palette) + guides(size = F) + theme(legend.position = "none")
pdf(paste(path.figures.acc, 'graph_te', prefix.mode[singleton.mode+1] ,'_big_acc_',acc,'.pdf', sep = ''), width = 5, height = 5)
print(p) # Plot 1 --> in the first page of PDF
dev.off()
}
p
sv.annot = read.table('/Volumes/Samsung_T5/vienn/work_sv/svs_annotation_v03.txt', stringsAsFactors = F)
rownames(sv.annot) = sv.annot$gr
head(sv.annot)
sv.annot[extracted_values,]
Big TE-nodes
n.amount = 20
g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)
size.big = g.nodes.cnt[b.graph.names]
alpha.big = rep(1, length(b.graph.names))
names(alpha.big) = b.graph.names
alpha.big[size.big < n.amount] = 0
sum(size.big >= n.amount)
set.seed(20)
p <- ggnet2(g.part, label = F, edge.color = "black",
node.size = size.big,
color = g.nodes.fam[b.graph.names],
alpha= alpha.big,
# mode = 'kamadakawai',
# arrow.gap = 0,
# arrow.size = 3,
palette = fam.palette) + guides(size = F) + guides(color = guide_legend(ncol = 2))
p
pdf(paste(path.figures, 'graph_small_cluster', prefix.mode[singleton.mode+1] ,'_family_amount.pdf', sep = ''), width = 5, height = 5)
print(p+ theme(legend.position = "none")) # Plot 1 --> in the first page of PDF
dev.off()
Which families specifically, and is the rate of insertion is different?
compare number of insertions with the total number of TE load
big.families = data.frame(node = names(size.big)[size.big >= n.amount])
big.families$size = size.big[big.families$node]
big.families$fam = g.nodes.fam[big.families$node]
big.families = big.families[order(-big.families$size),]
rownames(big.families) = NULL
node.big = nodes[nodes$node %in% big.families$node,]
v = read.table(paste(path.work, 'blast_sv_on_tes.txt', sep = ''))
v = v[v$V1 %in% node.big$te,]
pos.len1 = 2
pos.len2 = 5
v1.len = sapply(unique(v$V1), function(s) as.numeric(strsplit(s,'\\|')[[1]][pos.len1]))
v8.len = sapply(unique(v$V8), function(s) as.numeric(strsplit(s,'\\|')[[1]][pos.len2]))
v.len = c(v1.len, v8.len)
v.sim = findNestedness(v, use.strand = F)
v.sim = findNestedness(v, use.strand = F)
v.sim$p1 = v.sim$C1 / v.len[v.sim$V1]
v.sim$p8 = v.sim$C8 / v.len[v.sim$V8]
v.sim$p1.in8 = v.sim$C1 / v.len[v.sim$V8]
v.sim$p8.in1 = v.sim$C8 / v.len[v.sim$V1]
node.big$subfam = ''
for(sv.name in unique(v.sim$V1)){
v.tmp = v.sim[v.sim$V1 == sv.name,]
s = v.tmp$V8[which.max(v.tmp$p1)]
s = strsplit(s, '\\|')[[1]][8]
node.big[sv.name, 'subfam'] = s
}
x = tapply(node.big$subfam, node.big$node, function(x){
cnt = table(x)
x = names(cnt)[cnt == max(cnt)]
return(paste0(x, collapse = ','))
})
big.families$subfam = x[big.families$node]
no-TE SV
Construct
sv.se = readRDS(paste(path.svs, 'sv_se.rds', sep = ''))
sim.cutoff = 0.85
sv.se.no.te = sv.se$name[(sv.se$te == 'noTE') & (sv.se$len > 50)]
bl.file = paste(path.work,'sv_big_on_big.txt', sep = '')
bl.sv = read.table(bl.file, stringsAsFactors = F)
bl.sv = bl.sv[bl.sv$V1 != bl.sv$V8,]
# remove having TEs
bl.sv = bl.sv[bl.sv$V1 %in% sv.se.no.te, ]
bl.sv = bl.sv[bl.sv$V8 %in% sv.se.no.te, ]
pos.len1 = 2
sv.len = sapply(unique(c(bl.sv$V1, bl.sv$V8)), function(s) as.numeric(strsplit(s,'\\|')[[1]][pos.len1]))
bl.sv$len1 = sv.len[bl.sv$V1]
bl.sv$len8 = sv.len[bl.sv$V8]
max.len = 20000
bl.sv = bl.sv[(bl.sv$len1 <= max.len) & (bl.sv$len8 <= max.len),]
bl.sv$p1 = (bl.sv$V3 - bl.sv$V2 + 1) / bl.sv$len1
bl.sv$p8 = (abs(bl.sv$V5 - bl.sv$V4) + 1) / bl.sv$len8
bl.sv$comb = as.factor(paste(bl.sv$V1, bl.sv$V8, sep = '||'))
idx.mutual = (bl.sv$p1 >= sim.cutoff) & (bl.sv$p8 >= sim.cutoff)
# There is a big discussion in my head, whether it should be '&' or '|'
# If it's not ,utual, then maybe with something else it will construct a mutual relation,
# so we should remain for the analysis of nestedness all partial inclusions
sv.mutual = bl.sv[idx.mutual, ]
v = bl.sv[!idx.mutual, ]
v = v[!(v$comb %in% sv.mutual$comb),]
# At some point it was a step to remain only those instances which are not "unique" in combinations
# but I think it's not correct here
sv.sim = findNestedness(v, use.strand = T)
sv.sim$p1 = sv.sim$C1 / sv.len[sv.sim$V1]
sv.sim$p8 = sv.sim$C8 / sv.len[sv.sim$V8]
# here we should finally use '|', not '&'
sv.nested = sv.sim[(sv.sim$p1 >= sim.cutoff) | (sv.sim$p8 >= sim.cutoff) ,]
# Create pre-data for defining edges
common.names = intersect(colnames(sv.mutual), colnames(sv.nested))
sv.overall = rbind(sv.mutual[,common.names], sv.nested[,common.names])
sv.overall$group = (sv.overall$p1 >= sim.cutoff) * 1 + (sv.overall$p8 >= sim.cutoff) * 2
idx1 = sv.overall$group != 2 # V1 in V8
idx2 = sv.overall$group != 1 # V8 in V1
# Edges
sv.edges = rbind(cbind(sv.overall$V1[idx1], sv.overall$V8[idx1]),
cbind(sv.overall$V8[idx2], sv.overall$V1[idx2]))
sv.graph <- igraph::make_graph(t(sv.edges), directed = T)
sv.graph <- igraph::simplify(sv.graph)
sv.graphcomp <- igraph::components(sv.graph)
sv.memb = data.frame(memb = sv.graphcomp$membership)
sv.memb$name = rownames(sv.memb)
rownames(sv.memb) = NULL
rownames(sv.se) = sv.se$name
sv.memb$te = sv.se[sv.memb$name, 'te']
sv.memb$cover = sv.se[sv.memb$name, 'cover'] / sv.se[sv.memb$name, 'len']
sv.memb$len = sv.len[sv.memb$name]Plot all
g.part <- network(sv.edges, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)
set.seed(239)
p <- ggnet2(g.part, label = F, edge.color = "black",
node.size = 1,
# node.size = g.nodes.cnt[b.graph.names],
# color = g.nodes.type[b.graph.names],
# palette = te.cols
) + guides(size = F)
p
Plot with colors
sv.prot.init = readRDS(paste(path.work, 'sv_proteins_no_te_blast.rds', sep = ''))
sv.prot.init$name = sapply(sv.prot.init$X1, function(s){
s = paste0(strsplit(s, '\\|')[[1]][1:2], collapse = '|')
return(s)
})
sv.prot = sv.prot.init[sv.prot.init$prot == 1,]
sv.prot[,2] = tolower(sv.prot[,2])
types = c('disease', 'repeat', 'receptor', 'zinc', 'transcriptase', 'reverse', 'transpos')
for(i.type in 1:length(types)){
sv.prot[,types[i.type]] = (grepl(types[i.type], sv.prot[,2])) * 1
}
sv.prot$type = rowSums(sv.prot[,types])
table(sv.prot$type)
sv.memb$prot = 'no prot'
sv.memb$prot[sv.memb$name %in% sv.prot.init$name] = 'undefined prot'
sv.memb$prot[sv.memb$name %in% sv.prot$name] = 'defined prot'
for(type in types){
sv.memb$prot[sv.memb$name %in% sv.prot$name[sv.prot[,type] == 1]] = type
}
g.nodes.prot = sv.memb$prot
g.nodes.prot[g.nodes.prot == 'disease'] = 'defined prot'
names(g.nodes.prot) = sv.memb$name
set.seed(239)
p <- ggnet2(g.part, label = F, edge.color = "black",
# node.size = g.nodes.cnt[b.graph.names],
node.size = 1,
color = g.nodes.prot[b.graph.names],
palette = g.cols,
# mode = "kamadakawai"
) + guides(size = F) + coord_fixed(ratio = 1) +
scale_color_manual(values = g.cols,
breaks = c("transpos","reverse",
"repeat","zinc","receptor", "defined prot", "undefined prot",
"no prot"),
name = 'Protein key-word:') + theme(legend.justification = c(1, 0))
p = p+ theme(legend.key.height = unit(0.5, "cm"))
p
pdf(paste(path.figures, 'graph_new_all.pdf', sep = ''), width = 6, height = 4)
print(p) # Plot 1 --> in the first page of PDF
dev.off()
#
# cnt = table(g.nodes.prot)
# cnt = c(sum(cnt[c("transpos","reverse","repeat","zinc")]), sum(cnt[c("receptor","defined prot")]),
# cnt["undefined prot"], cnt["no prot"])
Types of the component
sv.graph <- igraph::make_graph(t(sv.edges), directed = T)
sv.graph <- igraph::simplify(sv.graph)
sv.graphcomp <- igraph::components(sv.graph)
sv.comp.member = sv.graphcomp$membership
s.tags = c("transpos","reverse","repeat","zinc", "receptor","defined prot", "undefined prot", 'no prot')
s.tags0 = rep('', length(s.tags))
s.tags0[1:4] = 'TE-like'
s.tags0[5:6] = 'Known Proteins'
s.tags0[7] = 'Undef. Proteins'
s.tags0[8] = 'No Proteins'
names(s.tags0) = s.tags
comp.tags = rep('', length(unique(sv.comp.member)))
for(s.tag in s.tags){
tmp.tags = unique(sv.comp.member[names(g.nodes.prot)[g.nodes.prot == s.tag]])
comp.tags[tmp.tags][comp.tags[tmp.tags] == ''] = s.tag
}
comp.tags[comp.tags == ''] = 'no prot'
comp.tags = data.frame(table(comp.tags))
colnames(comp.tags) = c('tag1', 'freq')
comp.tags$tag1 = factor(comp.tags$tag1, levels = s.tags)
comp.tags = comp.tags[order(comp.tags$tag1),]
comp.tags$tag0 = s.tags0[comp.tags$tag1]
comp.tags$tag0 = factor(comp.tags$tag0, levels = unique(s.tags0))
y.ticks = tapply(comp.tags$freq, comp.tags$tag0, sum)
y.ticks = y.ticks[!is.na(y.ticks)]
yy = sum(y.ticks) - cumsum(y.ticks) + y.ticks/2
comp.tags$ymin <- c(0, cumsum(comp.tags$freq)[-length(comp.tags$freq)])
comp.tags$ymax <- cumsum(comp.tags$freq)
x.step = rep(0, 8)
n.step = 10
x.step[c(5,7,8)] = n.step
x.step = cumsum(x.step)
comp.tags$ymin = comp.tags$ymin + x.step
comp.tags$ymax = comp.tags$ymax + x.step
y.min = tapply(comp.tags$ymin, comp.tags$tag0, min)
y.max = tapply(comp.tags$ymax, comp.tags$tag0, max)
y.val = (y.max + y.min) / 2
y.cnt = tapply(comp.tags$freq, comp.tags$tag0, sum)
df.text = data.frame(y.min = y.min, y.max = y.max, y.val = y.val, y.cnt = y.cnt, label = names(y.val))
df.text$angles <- 360 - (df.text$y.val / (max(comp.tags$ymax) + n.step)) * 360
df.text$angles[2:3] = 180 + df.text$angles[2:3]
p = ggplot(comp.tags, aes(x = 0, y = freq, fill = tag1)) +
geom_rect(aes(xmin = -0.5, xmax = 0.5, ymin = ymin, ymax = ymax)) +
coord_polar("y", start = 0) +
scale_fill_manual(values = g.cols.plus) + ylim(0, max(comp.tags$ymax) + n.step) +
theme_void() + xlim(-1.5, 0.7) +
geom_text(data=df.text, aes(x = 0.7, y = y.val, label = paste(label, y.cnt, sep = ': ')),
angle = df.text$angles, inherit.aes = FALSE) +
theme(legend.position="none") +
annotate("text", x = -1.5, y = 0, label = paste('Total',sum(comp.tags$freq),'\n connected \ncomponents'))
p
pdf(paste(path.figures, 'graph_new_pie_chart.pdf', sep = ''), width = 3.1, height = 3.1)
print(p) # Plot 1 --> in the first page of PDF
dev.off()
I don’t know
sv.se$freq = sv.se$freq.max
n.cutoff = 3
n = 28
sv.se$sin = 'indel'
sv.se$sin[sv.se$freq >= (n - n.cutoff)] = 'deletion'
sv.se$sin[sv.se$freq <= n.cutoff] = 'insertion'
g.nodes.prot.sin = g.nodes.prot
g.nodes.prot.sin[names(g.nodes.prot.sin) %in% sv.se$name[sv.se$sin != 'insertion'] ] = 'na'
g.cols['na'] = 'white'
set.seed(239)
p <- ggnet2(g.part, label = F, edge.color = "black",
# node.size = g.nodes.cnt[b.graph.names],
node.size = 1,
color = g.nodes.prot.sin[b.graph.names],
palette = g.cols,
# mode = "kamadakawai"
) + guides(size = F)
p
#
# path.figures = '/Volumes/Samsung_T5/vienn/work_te/'
# pdf(paste(path.figures, 'graph_sv_note_insertion.pdf', sep = ''), width = 6, height = 4)
# print(p) # Plot 1 --> in the first page of PDF
# dev.off()
alpha.edta = rep(1, length(b.graph.names))
names(alpha.edta) = b.graph.names
sv.annot.adta = rowSums(sv.annot[,11:ncol(sv.annot)] > 0.7) > 0
sv.annot.adta = sv.annot.adta[sv.se$gr]
names(sv.annot.adta) = sv.se$name
sv.annot.adta = sv.annot.adta[sv.annot.adta]
alpha.edta[names(alpha.edta) %in% names(sv.annot.adta)] = 0
set.seed(239)
p <- ggnet2(g.part, label = F, edge.color = "black",
# node.size = g.nodes.cnt[b.graph.names],
node.size = 1,
alpha=1-alpha.edta,
color = g.nodes.prot[b.graph.names],
palette = g.cols,
# mode = "kamadakawai"
) + guides(size = F)
p
pdf(paste(path.figures, 'graph_mob_note_edta.pdf', sep = ''), width = 6, height = 4)
print(p) # Plot 1 --> in the first page of PDF
dev.off()
path.figures = '/Volumes/Samsung_T5/vienn/work_te/'
pdf(paste(path.figures, 'graph_mob_note_edta_no_legend.pdf', sep = ''), width = 5, height = 5)
print(p+ theme(legend.position = "none")) # Plot 1 --> in the first page of PDF
dev.off()
Plot with component ID
tmp.graph <- igraph::make_graph(t(sv.edges), directed = T)
tmp.graph <- igraph::simplify(tmp.graph)
tmp.comp <- igraph::components(tmp.graph)
size.limit = 5
comp.id = as.character(tmp.comp$membership)
names(comp.id) = names(tmp.comp$membership)
comp.id[tmp.comp$csize[tmp.comp$membership] < size.limit] = ''
names.te = names(g.nodes.prot)[g.nodes.prot %in% c('transpos', 'reverse')]
comp.id[!(names(comp.id) %in% names.te)] = ''
comp.id[duplicated(comp.id)] = ''
comp.remain = as.numeric(comp.id[comp.id != ''])
alpha = rep(0, length(b.graph.names))
names(alpha) = names(tmp.comp$membership)
alpha[tmp.comp$membership %in% comp.remain] = 1
set.seed(239)
p <- ggnet2(g.part, label = comp.id[b.graph.names],
label.color = "black",
label.size = 3,
edge.color = "grey",
alpha = alpha[b.graph.names],
# node.size = g.nodes.cnt[b.graph.names],
node.size = 1,
color = g.nodes.prot[b.graph.names],
palette = g.cols,
# mode = "kamadakawai"
) + guides(size = F)
p
path.figures = '/Volumes/Samsung_T5/vienn/work_te/'
pdf(paste(path.figures, 'graph_sv_note_numbers.pdf', sep = ''), width = 5, height = 5)
print(p + theme(legend.position = "none")) # Plot 1 --> in the first page of PDF
dev.off()
# Order of components
cnt = table(tmp.comp$membership[tmp.comp$membership %in% comp.remain])
cnt = cnt[order(-cnt)]
CNV
cnv = readRDS('/Volumes/Samsung_T5/vienn/work_sv/similar_cnv_sv_on_accessions_cum_0.9.rds')
Plot one specific network
path.figures.examples = '/Volumes/Samsung_T5/vienn/work_te/examples/'
#
# tmp.graph <- igraph::make_graph(t(sv.edges), directed = T)
# tmp.graph <- igraph::simplify(tmp.graph)
# tmp.comp <- igraph::components(tmp.graph)
#
# tmp.cnt = table(tmp.comp$membership)
# tmp.cnt = -sort(-tmp.cnt)
tmp.cnt = cnt
for(k in 1:length(tmp.cnt)){
tmp.k = as.numeric(names(tmp.cnt)[k])
tmp.names = names(tmp.comp$membership)[tmp.comp$membership == tmp.k]
b.graph.sub = sv.edges[(sv.edges[,1] %in% tmp.names) &
(sv.edges[,2] %in% tmp.names),]
g.part.sub <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names.sub = network.vertex.names(g.part.sub)
b.graph.size.sub <- as.numeric(sub(".*\\|", "", b.graph.names.sub))
names(b.graph.size.sub) = b.graph.names.sub
# b.graph.size.sub = ceiling(log(b.graph.size.sub, 10))
if((length(unique( g.nodes.prot[b.graph.names.sub])) == 1)){
set.seed(20)
p <- ggnet2(g.part.sub, label = b.graph.size.sub[b.graph.names.sub], edge.color = "black",
node.size = 15,
arrow.gap = 0.07, arrow.size = 3,
color = g.cols[g.nodes.prot[b.graph.names.sub][1]],
) + guides(size = F) + ggtitle(paste('Component #', tmp.k))
p
} else {
set.seed(20)
p <- ggnet2(g.part.sub, label = b.graph.size.sub[b.graph.names.sub], edge.color = "black",
node.size = 15,
arrow.gap = 0.07, arrow.size = 3,
color = g.nodes.prot[b.graph.names.sub],
palette = g.cols,
) + guides(size = F) + ggtitle(paste('Component #', tmp.k))
p
}
pdf(paste(path.figures.examples, 'graph_sv_example_',k,'_comp_',tmp.k,'.pdf', sep = ''), width = 5, height = 4)
print(p + theme(legend.position = "none")) # Plot 1 --> in the first page of PDF
dev.off()
# annotation
annot.tmp = sv.prot[sv.prot$name %in% b.graph.names.sub,]
# annot.tmp = annot.tmp[annot.tmp$transpos == 1,]
write.table(annot.tmp, paste(path.figures.examples, 'graph_sv_example_',k,'_pblast.txt', sep = ''),
row.names = F, col.names = F, quote = F, sep = '\t')
# if EDTA annotation exists
sv.tmp = unique(c(b.graph.sub))
sv.tmp.cut <- gsub("\\|.*", "", sv.tmp)
sv.annot.tmp = sv.annot[sv.tmp.cut,]
n.fix = 9
sv.annot.tmp = sv.annot.tmp[,c(1:n.fix,n.fix+which(colSums(sv.annot.tmp[,(n.fix+1):ncol(sv.annot.tmp)]) != 0))]
rownames(sv.annot.tmp) = sv.tmp
write.table(sv.annot.tmp, paste(path.figures.examples, 'graph_sv_example_',k,'_edta.txt', sep = ''),
row.names = F, quote = F, sep = '\t')
# Copy0Number variation
cnv.tmp = cnv[sv.tmp,]
heatmap(cnv.tmp, col = colorRampPalette(c("white", "red"))(20))
}
Pie-chart of proteins
library(ggplot2)
data <- data.frame(
type = c("no proteins", "TE-related", "Категория 2", "Категория 3", "Категория 4"),
value = c(135, 63, 85, 133)
)
pie.chart <- ggplot(data, aes(x = "", y = value, fill = type)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0) +
theme_void()
pie.chart
Admixture groups
groups <- c(
"germany",
"south_sweden",
"north_sweden",
"south_sweden",
"north_sweden",
"germany",
"western_europe",
"central_europe",
"italy_balkan_caucasus",
"spain",
"relict",
"asia",
"central_europe",
"admixed",
"spain",
"relict",
"italy_balkan_caucasus",
"western_europe",
"asia",
"africa",
"china",
"china",
"africa",
"africa",
"madeira",
"madeira",
"africa"
)
# Используем функцию table() для подсчета количества элементов в каждой группе
as.matrix(table(groups))OLD
sunset <- colour("sunset")
discrete_rainbow <- colour("discrete rainbow")
file.te = '/Volumes/Samsung_T5/vienn/work/blast_tes_ann.txt'
sim.cutoff = 0.85
len.cutoff = 100
b = read.table(file.te, stringsAsFactors = F)
b = b[b$V1 != b$V8,]
b$len1 = as.numeric(sapply(b$V1, function(s) strsplit(s, '\\|')[[1]][7]))
b$len2 = as.numeric(sapply(b$V8, function(s) strsplit(s, '\\|')[[1]][7]))
b = b[b$len1 >= len.cutoff,]
b = b[b$len2 >= len.cutoff,]
b$comb = paste(b$V1, b$V8, sep = '^')
# Order positions in base
idx = b$V4 > b$V5
tmp = b[idx, 'V4']
b[idx, 'V4'] = b[idx, 'V5']
b[idx, 'V5'] = tmp
# --------------------------------------------------
# Get separately those, who has a unique coverage
comb.tbl = table(b$comb)
idx.uni = b$comb %in% names(comb.tbl)[comb.tbl == 1]
b.uni = b[idx.uni,]
b = b[!idx.uni,]
# This variable will be used later
b.uni$p1 = (b.uni$V3 - b.uni$V2 + 1) / b.uni$len1
b.uni$p2 = (b.uni$V5 - b.uni$V4 + 1) / b.uni$len2
b.uni = b.uni[(b.uni$p1 >= sim.cutoff) | (b.uni$p2 >= sim.cutoff),]
b.relations = data.frame(sub.te = b.uni$V1[b.uni$p1 >= sim.cutoff],
te = b.uni$V8[b.uni$p1 >= sim.cutoff], stringsAsFactors = F)
b.relations = rbind(b.relations,
data.frame(sub.te = b.uni$V8[b.uni$p2 >= sim.cutoff],
te = b.uni$V1[b.uni$p2 >= sim.cutoff], stringsAsFactors = F))
b.relations = unique(b.relations)
# --------------------------------------------------
# Min-max of the coverage to remove those, who are NOT in each other completely
b.cov = tapply(b$V2, b$comb, min)
b.cov = data.frame(comb = names(b.cov), V2 = b.cov)
b.cov$V3 = tapply(b$V3, b$comb, max)
b.cov$V4 = tapply(b$V4, b$comb, min)
b.cov$V5 = tapply(b$V5, b$comb, max)
b.cov$len1 = tapply(b$len1, b$comb, unique)
b.cov$len2 = tapply(b$len2, b$comb, unique)
b.cov$p1 = (b.cov$V3 - b.cov$V2 + 1) / b.cov$len1
b.cov$p2 = (b.cov$V5 - b.cov$V4 + 1) / b.cov$len2
comb.uncov = b.cov$comb[(b.cov$p1 < sim.cutoff) & (b.cov$p2 < sim.cutoff)]
b = b[!(b$comb %in% comb.uncov),]
# --------------------------------------------------
# Calculate the coverage directly for the first
b = b[order(b$V3),]
b = b[order(b$V2),]
b = b[order(b$comb),]
# Remove nested
idx = which((b$V3[-nrow(b)] > b$V3[-1]) & (b$comb[-nrow(b)] == b$comb[-1])) + 1
b1 = b[-idx,]
# Compute gaps
b1$gap = c(b1$V2[-1] - b1$V3[-nrow(b1)] - 1, 0)
b1$gap[b1$gap < 0] = 0
idx.diff.comb = which(b1$comb[-1] != b1$comb[-nrow(b1)])
b1$gap[idx.diff.comb] = 0
b.cov = tapply(b1$V2, b1$comb, min)
b.cov = data.frame(comb = names(b.cov), V2 = b.cov)
b.cov$V3 = tapply(b1$V3, b1$comb, max)
b.cov$len1 = tapply(b1$len1, b1$comb, unique)
b.cov$gap = tapply(b1$gap, b1$comb, sum)
b.cov$len1 = b.cov$len1
b.cov$p1 = (b.cov$V3 - b.cov$V2 + 1 - b.cov$gap) / b.cov$len1
b.cov$V1 = tapply(b1$V1, b1$comb, unique)
b.cov$V8 = tapply(b1$V8, b1$comb, unique)
b.cov = b.cov[b.cov$p1 >= sim.cutoff,]
b.relations = rbind(b.relations,
data.frame(sub.te = b.cov$V1,
te = b.cov$V8, stringsAsFactors = F))
# --------------------------------------------------
# Calculate the coverage directly for the second
b = b[order(b$V5),]
b = b[order(b$V4),]
b = b[order(b$comb),]
# Remove nested
idx = which((b$V5[-nrow(b)] > b$V5[-1]) & (b$comb[-nrow(b)] == b$comb[-1])) + 1
b1 = b[-idx,]
# Compute gaps
b1$gap = c(b1$V4[-1] - b1$V5[-nrow(b1)] - 1, 0)
b1$gap[b1$gap < 0] = 0
idx.diff.comb = which(b1$comb[-1] != b1$comb[-nrow(b1)])
b1$gap[idx.diff.comb] = 0
b.cov = tapply(b1$V4, b1$comb, min)
b.cov = data.frame(comb = names(b.cov), V4 = b.cov)
b.cov$V5 = tapply(b1$V5, b1$comb, max)
b.cov$len2 = tapply(b1$len2, b1$comb, unique)
b.cov$gap = tapply(b1$gap, b1$comb, sum)
b.cov$len2 = b.cov$len2
b.cov$p1 = (b.cov$V5 - b.cov$V4 + 1 - b.cov$gap) / b.cov$len2
b.cov$V1 = tapply(b1$V1, b1$comb, unique)
b.cov$V8 = tapply(b1$V8, b1$comb, unique)
b.cov = b.cov[b.cov$p1 >= sim.cutoff,]
b.relations = rbind(b.relations,
data.frame(sub.te = b.cov$V8,
te = b.cov$V1, stringsAsFactors = F))
b.relations = unique(b.relations)
b.relations
Define clusters
b.nodes = rbind(b.relations,
data.frame(sub.te = b.relations$te,
te = b.relations$sub.te))
b.nodes$comb = paste(b.nodes$sub.te, b.nodes$te, sep = '^')
comb.tbl = table(b.nodes$comb)
comb.back.and.foth = names(comb.tbl)[comb.tbl >= 2]
b.nodes = b.nodes[b.nodes$comb %in% comb.back.and.foth,]
b.nodes = unique(b.nodes[, c('sub.te', 'te')])
te.nodes <- igraph::make_graph(t(b.nodes), directed = T)
te.nodes <- igraph::simplify(te.nodes)
te.nodes.comp <- igraph::components(te.nodes)
nodes = paste('N', te.nodes.comp$membership, sep = '')
names(nodes) = names(te.nodes.comp$membership)Identify family for each node
nodes.family = sapply(names(nodes), function(s) strsplit(s, '\\|')[[1]][6])
nodes.family.max = tapply(nodes.family, nodes, function(s){
tbl = table(s)
f = names(tbl)[tbl == max(tbl)]
if(length(f) == 1){
return(f)
} else {
return('Mix')
}
})
nodes.family.max[nodes.family.max %in% c('DNA/Pogo', 'DNA/Tc1', 'DNA/Harbinger', 'DNA/En-Spm',
'DNA/HAT', 'DNA', 'DNA/Mariner')] = 'DNA'
nodes.family.max[nodes.family.max %in% c('RathE1_cons', 'RathE2_cons')] = 'DNA'
nodes.family.max[nodes.family.max %in% c('LINE/L1', 'LINE?')] = 'LINE'
nodes.family.max[nodes.family.max %in% c('Unassigned')] = 'Mix'
nodes.family.unique = unique(nodes.family.max)
Graph without singletons
b.graph.init = b.relations[(b.relations$sub.te %in% names(nodes)) & (b.relations$te %in% names(nodes)),]
b.graph = b.graph.init
b.graph = cbind(nodes[as.character(b.graph$sub.te)], nodes[as.character(b.graph$te)])
b.graph = unique(b.graph)
b.graph = b.graph[b.graph[,1] != b.graph[,2],]
# reduce indirect arrows
idx.remove = c()
for(i.edge in 1:nrow(b.graph)){
if(i.edge %% 1000 == 0) print(i.edge)
tmp.to = b.graph[b.graph[,1] == b.graph[i.edge,1],2]
tmp.from = b.graph[b.graph[,2] == b.graph[i.edge,2],1]
if(length(intersect(tmp.to, tmp.from)) > 0) idx.remove = c(idx.remove, i.edge)
}
idx.remove = unique(idx.remove)
b.graph = b.graph[-idx.remove,]
# te.graph <- igraph::make_graph(t(b.graph), directed = T)
# te.graph <- igraph::simplify(te.graph)
# te.graph.comp <- igraph::components(te.graph)
nodes.family.max.graph = nodes.family.max[names(nodes.family.max) %in% unique(c(b.graph[,1], b.graph[,2]))]
graph.cols = sunset(length(unique(nodes.family.max.graph)))
graph.cols = discrete_rainbow(length(unique(nodes.family.max.graph)))
names(graph.cols) = unique(nodes.family.max.graph)
g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
p <- ggnet2(g.part, label = FALSE, edge.color = "black", node.size = 1,
color = nodes.family.max.graph, palette = graph.cols,
mode = "kamadakawai")# + guides(size = FALSE)
p
Graph WITH singletons
names.core = names(nodes.family.max.graph)
b.graph.init = b.relations
for(i in 1:2){
b.graph.init[b.graph.init[,i] %in% names(nodes), i] = nodes[b.graph.init[b.graph.init[,i] %in% names(nodes), i]]
}
b.graph = unique(b.graph.init)
b.graph = b.graph[b.graph[,1] != b.graph[,2],]
b.graph = unique(b.graph)
# Verteces from the previous graph
b.graph = b.graph[(b.graph[,1] %in% names.core) | (b.graph[,2] %in% names.core),]
# reduce indirect arrows
idx.remove = c()
for(i.edge in 1:nrow(b.graph)){
if(i.edge %% 1000 == 0) print(i.edge)
tmp.to = b.graph[b.graph[,1] == b.graph[i.edge,1],2]
tmp.from = b.graph[b.graph[,2] == b.graph[i.edge,2],1]
if(length(intersect(tmp.to, tmp.from)) > 0) idx.remove = c(idx.remove, i.edge)
}
idx.remove = unique(idx.remove)
b.graph = b.graph[-idx.remove,]
te.graph <- igraph::make_graph(t(b.graph), directed = T)
d <- igraph::distances(te.graph)
# te.graph <- igraph::simplify(te.graph)
# te.graph.comp <- igraph::components(te.graph)
names.new = unique(setdiff(c(b.graph[,1], b.graph[,2]), names(nodes.family.max)))
# names.new.val = paste('G',1:length(names.new), sep = '')
# names(names.new.val) = names.new
# names.new.val =
names.new.family = sapply(names.new, function(s) strsplit(s, '\\|')[[1]][6])
names.new.family[names.new.family %in% c('DNA/Pogo', 'DNA/Tc1', 'DNA/Harbinger', 'DNA/En-Spm',
'DNA/HAT', 'DNA', 'DNA/Mariner')] = 'DNA'
names.new.family[names.new.family %in% c('RathE1_cons', 'RathE2_cons')] = 'DNA'
names.new.family[names.new.family %in% c('LINE/L1', 'LINE?')] = 'LINE'
names.new.family[names.new.family %in% c('Unassigned')] = 'Mix'
nodes.family.max.add = c(nodes.family.max, names.new.family)
nodes.family.max.add = nodes.family.max.add[unique(c(b.graph[,1], b.graph[,2]))]
graph.cols = discrete_rainbow(length(unique(nodes.family.max.add)))
graph.cols = sample(graph.cols)
names(graph.cols) = unique(nodes.family.max.add)
g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
p <- ggnet2(g.part, label = FALSE, edge.color = "black", node.size = 0.5,
color = nodes.family.max.add,
palette = graph.cols, mode = "kamadakawai")
pTSNE
library(Rtsne)
d <- igraph::distances(te.graph)
d.max = max(d[!is.infinite(d)])
d[is.infinite(d)] = d.max * 1.3
tSNE <- Rtsne(d, is_distance = TRUE, dims = 2)
plot(tSNE$Y[,1], tSNE$Y[,2])
---
title: "Graph of TEs"
output: html_notebook
---


# Setup
```{r, message=FALSE}
# library(ggplot2)
# library(reshape2)
library(ggpattern)
library(viridis)
library(colorRamps)
library(gridExtra)
library(ggplot2)
library('igraph')
library(ggnet)
library(network)
library(khroma)
library(dplyr)
library(ggpubr)
library(Biostrings)

source('similarity.R')
source('seqdotplot.R')
source('msaplot.R')

sunset <- colour("sunset")
discrete_rainbow <- colour("discrete rainbow")

path.base = '../../../'
path.work = paste(path.base, '02_analysis/04_sv/01_data/', sep = '')
path.tair = paste(path.base, '01_data_common/01_tair10/', sep = '')
path.figures = paste(path.base, '02_analysis/04_sv/03_figures/', sep = '')
path.svs = paste(path.base, '01_data_common/02_annot_denovo/02_pannagram/svs/', sep = '')
# path.genes = paste(path.base, '01_data_common/02_annot_denovo/02_pannagram/genes/', sep = '')

# sim.cutoff = 0.9

sim.cutoff = 0.85

```

# Colors
```{r}

# ---- Colors of TE content ----
te.content.names = c("noTE", "isTE", "hasTE", "hasTEpart", "isTEpart")
te.cols = c('#D8D9CF', '#EB455F', '#7B6079', '#3C8DAD', '#79B773')
names(te.cols) = te.content.names


# ---- Colors of BLAST hits ----
colors.blast.hits <- c("in graph" = "#676FA3",
            "partial overlap" = "#FF9F29",
            "1 self-hit" = "#6EBF8B",
            "0 hits" = "#D82148",
            "in graph but not in SVs" = "#151D3B")


# ---- Colors of known TE families ----
fam.palette = c()
fam.palette['Unassigned'] = 'grey'
fam.palette['Mix'] = 'grey20'
fam.palette['Mix with Helitron'] = '#266D98'
fam.palette['Helitron'] = '#BCACDE'
fam.palette["LTR/Copia"] = '#BFDB38'
fam.palette["LTR/Gypsy"] = '#54B435'
fam.palette["DNA/HAT"] = '#F9B5D0'
fam.palette["DNA+"] = '#C8658C'
fam.palette["DNA"] = '#C8658C'
fam.palette["DNA/MuDR"] = '#971549'


fam.palette["LINE"] = '#FFC26F'
fam.palette["RathE1/2/3_cons"] = '#C38154'
fam.palette["SINE"] = '#884A39'
fam.palette["TEG"] = '#4E3636'


# Colors for new TEs
g.cols = discrete_rainbow(length(unique(sv.memb$prot)))
names(g.cols) = unique(sv.memb$prot)
# names(g.cols) = c('no prot', "undefined prot", "reverse", "transpos","repeat","zinc", "receptor","defined prot")

g.cols['disease'] = 'black'
# g.cols['defined prot'] = '#F0B86E'
# g.cols['repeat'] = '#CAE0AB'
# g.cols['zink'] = '#1A5D1A'
# g.cols['receptor'] = '#ED7B7B'

g.cols['zinc'] = '#F45050'
g.cols['repeat'] = '#EA906C'
g.cols['receptor'] = '#F7D060'
g.cols['reverse'] = '#98D8AA'
g.cols['defined prot'] = '#7BAFDE'



```



# TEs
```{r}

# Load similarity function

bl.file = paste(path.work,'new_te_on_te.fasta',sep = '')
bl.res = read.table(bl.file)
bl.res = bl.res[bl.res$V1 != bl.res$V8,]

bl.res.init = bl.res
bl.res = bl.res[bl.res$V6 >= sim.cutoff * 100,]

res.nest = findNestedness(bl.res, use.strand = F)

res.nest.len = sapply(unique(c(res.nest$V1, res.nest$V8)), function(s) as.numeric(strsplit(s, '\\|')[[1]][5]))
  
res.nest$len1 = res.nest.len[res.nest$V1]
res.nest$len8 = res.nest.len[res.nest$V8]
res.nest$p1 = res.nest$C1 / res.nest$len1
res.nest$p8 = res.nest$C8 / res.nest$len8

res.nest.sim = res.nest[(res.nest$p1 >= sim.cutoff) | 
                          (res.nest$p8 >= sim.cutoff),]
```

## How many TEs are in the graph
Distribution among families and subfamilies
Distribution among lengths
```{r}
te.in.graph = unique(c(res.nest$V1, res.nest$V8))

# What is the actual number of TEs
file.content <- readLines(bl.file)

selected.lines <- file.content[grepl("^# Query:|hits found", file.content)]
df.query = data.frame(b.query=selected.lines[seq(1, length(selected.lines), by = 2)],
                      b.hits=selected.lines[seq(2, length(selected.lines), by = 2)])

df.query$query  <- gsub("^# Query: (.*)", "\\1", df.query$b.query)
df.query$len <- as.numeric(sapply(strsplit(df.query$query, "\\|"), function(x) x[5]))
df.query$hits <- as.numeric(stringr::str_extract(df.query$b.hits, "\\d+"))
df.query$val.hits = df.query$hits
df.query$val.hits[df.query$val.hits >= 2] = 2
df.query$val.hits[df.query$query %in% bl.res$V8] = 2
df.query$val.hits[df.query$query %in% te.in.graph] = 3
hit.values = c('0 hits', '1 self-hit', 'partial overlap', 'in graph', "in graph but not in SVs")
df.query$s.hits = hit.values[df.query$val.hits+1]
df.query$s.hits = factor(df.query$s.hits, levels = rev(hit.values))
df.query$family <- sapply(strsplit(df.query$query, "\\|"), function(x) x[9])
df.query$subfam <- sapply(strsplit(df.query$query, "\\|"), function(x) x[8])



# TEs, which are not in SVs
te.in.svs = read.table(paste(path.work, 'blast_tes_on_sv.txt', sep = ''), stringsAsFactors = F)
te.rest = setdiff(df.query$query, te.in.svs$V1)
te.in.svs = read.table(paste(path.work, 'blast_sv_on_tes.txt', sep = ''), stringsAsFactors = F)
te.rest = setdiff(te.rest, te.in.svs$V8)
df.query$s.hits[df.query$query %in% te.rest] = "in graph but not in SVs"


p = ggplot(df.query, aes(x = len, fill = s.hits, color = s.hits)) +
  # geom_histogram(aes(y = ..density..), alpha=0.5, color = "black", bins = 30) +
  # geom_jitter(height = 0.02, width = 0, alpha = 0.7) +
  geom_density(alpha = 0.5) +
  scale_fill_manual(values = colors.blast.hits) +
  scale_color_manual(values = colors.blast.hits) +
   scale_x_log10() +
  labs(fill = NULL, color = NULL) +
  xlab('length of TEs') + ylab('Normalised density') +
  theme_minimal() +
  theme(legend.position = c(1, 1), legend.justification = c(1, 1),
          legend.background = element_rect(color = "grey90"))

p

pdf(paste(path.figures, 'tes_self_blast_len_density.pdf', sep = ''), width = 5, height = 4)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()


table(df.query$val.hits)
```


### TEs not in SVs
```{r}
# TEs in te-graph: te.in.graph
# TEs which are have no connection to SVs

df = as.data.frame(table(df.query$s.hits))

p = ggplot(df, aes(x = "", y = Freq, fill = Var1)) +
  geom_bar(stat="identity", width=1, alpha = 0.7) +
  coord_polar("y", start=0) +
  labs(title=NULL, fill="Categories") +
  theme_void()+
    scale_fill_manual(values = colors.blast.hits) +
  geom_text(aes(label = Freq,x = 1.3), position = position_stack(vjust = 0.5)) + theme(legend.position="none")
p

pdf(paste(path.figures, 'tes_self_blast_pie_chart.pdf', sep = ''), width = 3, height = 3)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

```

## Examples
### Examples no hits
```{r}


df.query.tmp = df.query[(df.query$val.hits == '0'),]

cnt.init = c(table(df.query$family))
cnt.tmp = c(table(df.query.tmp$family))

common_names <- intersect(names(cnt.init), names(cnt.tmp))
# Создание dataframe только для совпадающих имен
df_match <- data.frame(names = common_names, values.init = cnt.init[common_names], 
                       values.tmp = cnt.tmp[common_names])


gradient_colors <- c(discrete_rainbow(nrow(df_match)))
names(gradient_colors) = NULL


p = ggplot(df_match, aes(x = values.init, y = values.tmp, label = names, color = names)) +
  geom_point() +
  # geom_text(hjust = 0, vjust = 0) +
  ggrepel::geom_text_repel(max.overlaps = 20) +
  xlab("Initial counts") +
  ylab("counts of No hits") +
  scale_x_log10() +
  scale_y_log10() +
  scale_color_manual(values = gradient_colors) +
  theme(legend.position = "none") +
  guides(color = FALSE) +
  theme_minimal()
p


pdf(paste(path.figures, 'tes_self_scatter_no_hits.pdf', sep = ''), width = 5, height = 4)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

# No hits and long
len.min = 100
df.query.tmp = df.query[(df.query$val.hits == '0') & (df.query$len >= len.min),]


cnt.init = c(table(df.query$family))
cnt.tmp = c(table(df.query.tmp$family))

common_names <- intersect(names(cnt.init), names(cnt.tmp))
# Создание dataframe только для совпадающих имен
df_match <- data.frame(names = common_names, values.init = cnt.init[common_names], 
                       values.tmp = cnt.tmp[common_names])


gradient_colors <- c(discrete_rainbow(nrow(df_match)))
names(gradient_colors) = NULL

p = ggplot(df_match, aes(x = values.init, y = values.tmp, label = names, color = names)) +
  geom_point() +
  # geom_text(hjust = 0, vjust = 0) +
  # ggrepel::geom_text_repel(max.overlaps = 20) +
  ggrepel::geom_text_repel(aes(label = paste(names,'(',values.tmp,')',sep ='')), max.overlaps = 20) +
  xlab("Initial counts") +
  ylab("counts of No hits") +
  scale_x_log10() +
  scale_y_log10() +
  scale_color_manual(values = gradient_colors) +
  theme(legend.position = "none") +
  guides(color = FALSE) +
  theme_minimal() +
  geom_text(aes(x=0,y=Inf,hjust=0, vjust=3,
                label=paste('Length >=', len.min)), color = 'grey20')
p


pdf(paste(path.figures, 'tes_self_scatter_no_hits_long.pdf', sep = ''), width = 5, height = 4)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()


```

```{r}

head(df.query.tmp[df.query.tmp$family == 'DNA/MuDR',]$query)
```


### Examples one self-hit
#### families
```{r}


df.query.tmp = df.query[(df.query$val.hits == 1),]

cnt.init = c(table(df.query$family))
cnt.tmp = c(table(df.query.tmp$family))

common_names <- intersect(names(cnt.init), names(cnt.tmp))
# Создание dataframe только для совпадающих имен
df_match <- data.frame(names = common_names, values.init = cnt.init[common_names], 
                       values.tmp = cnt.tmp[common_names])


gradient_colors <- c(discrete_rainbow(nrow(df_match)))
names(gradient_colors) = NULL


p = ggplot(df_match, aes(x = values.init, y = values.tmp, label = names, color = names)) +
  geom_point() +
  # geom_text(hjust = 0, vjust = 0) +
  ggrepel::geom_text_repel(max.overlaps = 20) +
  xlab("Initial counts") +
  ylab("Counts in \"1 self-hits\" category") +
  scale_x_log10() +
  scale_y_log10() +
  scale_color_manual(values = gradient_colors) +
  theme(legend.position = "none") +
  guides(color = FALSE) +
  theme_minimal()
p


pdf(paste(path.figures, 'tes_self_scatter_1_selfhits_fam.pdf', sep = ''), width = 5, height = 4)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()
```

#### subfamilies
```{r}


df.query.tmp = df.query[(df.query$val.hits == 1) & (df.query$len >= 600),]

cnt.init = c(table(df.query$subfam))
cnt.tmp = c(table(df.query.tmp$subfam))

common_names <- intersect(names(cnt.init), names(cnt.tmp))
# Создание dataframe только для совпадающих имен
df_match <- data.frame(names = common_names, values.init = cnt.init[common_names], 
                       values.tmp = cnt.tmp[common_names])


# gradient_colors <- c(discrete_rainbow(nrow(df_match)))
names(gradient_colors) = NULL


p = ggplot(df_match, aes(x = values.init, y = values.tmp, label = names, color = names)) +
  geom_point() +
  # geom_text(hjust = 0, vjust = 0) +
  ggrepel::geom_text_repel(max.overlaps = 20) +
  xlab("Initial counts") +
  ylab("Counts in \"1 self-hits\" category") +
  scale_x_log10() +
  scale_y_log10() +
  # scale_color_manual(values = gradient_colors) +
  theme(legend.position = "none") +
  guides(color = FALSE) +
  theme_minimal()
p


pdf(paste(path.figures, 'tes_self_scatter_1_selfhits_subfam.pdf', sep = ''), width = 7, height = 5)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()


```

#### individuals from subfamilies
```{r}
s.subfam = 'ATREP8'

df.query.tmp = df.query[(df.query$subfam == s.subfam) & (df.query$len >= 600),]
df.query.tmp


```





## Creating the graph
```{r}
# all edges
idx = res.nest$p1 >= sim.cutoff
edges = cbind(res.nest$V1[idx], res.nest$V8[idx])
idx = res.nest$p8 >= sim.cutoff
edges = rbind(edges, cbind(res.nest$V8[idx], res.nest$V1[idx]))
te.enges.names = unique(c(edges[,1], edges[,2]))
te.enges.fam = sapply(te.enges.names, function(s) strsplit(s, '\\|')[[1]][9] )

te.enges.fam[te.enges.fam %in% c('DNA/Pogo', 'DNA/Tc1', 'DNA/Harbinger', 'DNA/En-Spm',
                     'DNA/HAT', 'DNA', 'DNA/Mariner')] = 'DNA'
te.enges.fam[te.enges.fam %in% c('RathE1_cons', 'RathE2_cons', 'RathE3_cons')] = 'RathE1/2/3_cons'
te.enges.fam[te.enges.fam %in% c('LINE/L1', 'LINE?')] = 'LINE'
te.enges.fam[te.enges.fam %in% c('Unassigned')] = 'Mix'
te.enges.fam[te.enges.fam %in% c('RC/Helitron')] = 'Helitron'

edges = edges[te.enges.fam[edges[,1]] != 'TEG',]
edges = edges[te.enges.fam[edges[,2]] != 'TEG',]
te.enges.names = unique(c(edges[,1], edges[,2]))


# nodes
idx = (res.nest$p1 >= sim.cutoff) & (res.nest$p8 >= sim.cutoff)
te.nodes = cbind(res.nest$V1[idx], res.nest$V8[idx])
te.nodes = te.nodes[te.enges.fam[te.nodes[,1]] != 'TEG',]
te.nodes = te.nodes[te.enges.fam[te.nodes[,2]] != 'TEG',]

te.rest = setdiff(te.enges.names, c(te.nodes[,1], te.nodes[,2]))


te.nodes.graph <- igraph::make_graph(t(te.nodes), directed = T)
te.nodes.graph <- igraph::simplify(te.nodes.graph)
te.nodes.comp <- igraph::components(te.nodes.graph)

nodes = data.frame(node = paste('N', te.nodes.comp$membership, sep = ''), 
                   te = names(te.nodes.comp$membership))

nodes.rest = data.frame(node = paste('R', (1:length(te.rest)), sep = ''), te = te.rest)
nodes = rbind(nodes, nodes.rest)

rownames(nodes) = nodes$te


nodes.cnt = data.frame(cnt = c(table(nodes$node)))
nodes.cnt$node = rownames(nodes.cnt)
nodes.cnt$fam = sapply(nodes.cnt$node, function(s){
  s.te = nodes$te[nodes$node == s]
  fam.te = unique(te.enges.fam[s.te])
  if(length(fam.te) == 1){
    return(fam.te)
  } else {
    fam.te = setdiff(fam.te, 'TEG')
    if(length(fam.te) == 1) return(fam.te)
    return('Mix')
  }
})
table(nodes.cnt$fam)


# Redefine edges but with node names
idx.endes = (edges[,1] %in% nodes$te) & (edges[,2] %in% nodes$te)
b.graph = cbind(nodes[edges[idx.endes,1], 'node'],nodes[edges[idx.endes,2], 'node'])
b.graph = unique(b.graph)
# b.graph = b.graph[b.graph[,1] != b.graph[,2],]
b.graph.uni = b.graph[b.graph[,1] == b.graph[,2],]
b.graph = b.graph[b.graph[,1] != b.graph[,2],]

length(unique(c(b.graph[,1], b.graph[,2])))

# reduce indirect arrows
idx.remove = c()
for(i.edge in 1:nrow(b.graph)){
  if(i.edge %% 1000 == 0) print(i.edge)
  tmp.to = b.graph[b.graph[,1] == b.graph[i.edge,1],2]
  tmp.from = b.graph[b.graph[,2] == b.graph[i.edge,2],1]
  if(length(intersect(tmp.to, tmp.from)) > 0) idx.remove = c(idx.remove, i.edge)
}
idx.remove = unique(idx.remove)
b.graph = b.graph[-idx.remove,]
# b.graph = rbind(b.graph, b.graph.uni)

# Print graph

g.nodes.fam = nodes.cnt$fam
names(g.nodes.fam) = nodes.cnt$node
g.nodes.cnt = nodes.cnt$cnt
names(g.nodes.cnt) = nodes.cnt$node

g.cols = discrete_rainbow(length(unique(g.nodes.fam)))
names(g.cols) = unique(g.nodes.fam)

b.graph.init = b.graph


g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)
```

### Old colors
```{r}
# p <- ggnet2(g.part, label = F, edge.color = "black", 
#             node.size = g.nodes.cnt[b.graph.names], 
#             color = g.nodes.fam[b.graph.names],
#             palette = g.cols,
#             # mode = "kamadakawai"
#             ) 
# p + guides(size = F)
# 
# # 
# # b.graph.fam = cbind(g.nodes.fam[b.graph[,1]], g.nodes.fam[b.graph[,2]])
# # b.graph.fam
# # 
# # which((b.graph.fam[,1] == 'DNA/MuDR') & (b.graph.fam[,1] == 'LINE'))
# 


```

### New Family colors
```{r}
# g.fam.names = sort(unique(g.nodes.fam))
# fam.palette = c()
# idx.pallete = c()
# 
# idx.fam <- grep("^Helitron", g.fam.names, value = FALSE)
# tmp.palette <- colorRampPalette(c('#BFACE2', '#266D98', '#422B72'))(length(idx.fam))
# idx.pallete = c(idx.pallete, idx.fam)
# fam.palette = c(fam.palette, tmp.palette)
# 
# idx.fam <- grep("^LTR", g.fam.names, value = FALSE)
# tmp.palette <- colorRampPalette(c('#BFDB38', '#54B435'))(length(idx.fam))
# idx.pallete = c(idx.pallete, idx.fam)
# fam.palette = c(fam.palette, tmp.palette)
# 
# idx.fam <- grep("^DNA", g.fam.names, value = FALSE)
# tmp.palette <- colorRampPalette(c('#F9B5D0', '#971549'))(length(idx.fam))
# idx.pallete = c(idx.pallete, idx.fam)
# fam.palette = c(fam.palette, tmp.palette)
# 
# idx.fam = setdiff(1:length(g.fam.names), idx.pallete)
# tmp.palette <- colorRampPalette(c('#FFC26F', '#C38154', '#884A39', '#4E3636'))(length(idx.fam))
# idx.pallete = c(idx.pallete, idx.fam)
# fam.palette = c(fam.palette, tmp.palette)
# 
# names(fam.palette) = g.fam.names[idx.pallete]
# fam.palette['Unassigned'] = 'grey'
# fam.palette['Mix'] = 'black'
# fam.palette['TEG'] = 'darkgreen'




```

```{r}

set.seed(20)
p <- ggnet2(g.part, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names], 
            color = g.nodes.fam[b.graph.names],
            palette = fam.palette,
            # mode = "kamadakawai"
            ) 
p = p + guides(size = F) 
p = p + coord_fixed(ratio = 1)
p



pdf(paste(path.figures, 'graph_tes_family.pdf', sep = ''), width = 5, height = 5)
print(p+ theme(legend.position = "none"))     # Plot 1 --> in the first page of PDF
dev.off()

pdf(paste(path.figures, 'graph_tes_family_legend.pdf', sep = ''), width = 7, height = 5)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

```



## Separately visualise connected components
```{r}
tmp.graph <- igraph::make_graph(t(b.graph), directed = T)
tmp.graph <- igraph::simplify(tmp.graph)
tmp.comp <- igraph::components(tmp.graph)

tmp.cnt = table(tmp.comp$membership)
tmp.cnt = -sort(-tmp.cnt)
head(tmp.cnt)

k = 1
tmp.k = as.numeric(names(tmp.cnt)[k])
tmp.names = names(tmp.comp$membership)[tmp.comp$membership == tmp.k]
b.graph.sub = b.graph[(b.graph[,1] %in% tmp.names) & 
                        (b.graph[,2] %in% tmp.names),]

g.part.sub.big <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names.sub.big = network.vertex.names(g.part.sub.big)


set.seed(20)
p <- ggnet2(g.part.sub.big, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names.sub.big], 
            color = g.nodes.fam[b.graph.names.sub.big],
            mode = 'kamadakawai',
            palette = fam.palette) + guides(size = F)
p.big.type = p + theme(legend.position = "none")

# set.seed(20)
# p <- ggnet2(g.part.sub.big, label = F, edge.color = "black", 
#             node.size = g.nodes.cnt[b.graph.names.sub.big], 
#             color = g.nodes.fam[b.graph.names.sub.big],
#             mode = 'kamadakawai',
#             palette = fam.palette) + guides(size = F)
# p.big.color = p + theme(legend.position = "none")


tmp.k = as.numeric(names(tmp.cnt)[k])
tmp.names = names(tmp.comp$membership)[tmp.comp$membership != tmp.k]
b.graph.sub = b.graph[(b.graph[,1] %in% tmp.names) & 
                        (b.graph[,2] %in% tmp.names),]

g.part.sub.small <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names.sub.small = network.vertex.names(g.part.sub.small)


set.seed(20)
p <- ggnet2(g.part.sub.small, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names.sub.small], 
            color = g.nodes.fam[b.graph.names.sub.small],
            # mode = 'kamadakawai',
            palette = fam.palette) + guides(size = F)
p.small.type =p + theme(legend.position = "none")

# set.seed(20)
# p <- ggnet2(g.part.sub.small, label = F, edge.color = "black", 
#             node.size = g.nodes.cnt[b.graph.names.sub.small], 
#             color = g.nodes.fam[b.graph.names.sub.small],
#             # mode = 'kamadakawai',
#             palette = fam.palette) + guides(size = F)
# p.small.color = p + theme(legend.position = "none")



```
### Plots
```{r}
p.big.type
p.small.type


pdf(paste(path.figures, 'graph_tes_family_small.pdf', sep = ''), width = 9, height = 9)
print(p.small.type)     # Plot 1 --> in the first page of PDF
dev.off()

pdf(paste(path.figures, 'graph_tes_family_big.pdf', sep = ''), width = 5, height = 5)
print(p.big.type)     # Plot 1 --> in the first page of PDF
dev.off()

```

# Stop for the paper
```{r}
stop()
```


## Specific TE families
### Graph of one family

```{r}
sort(-table(df.query$subfam[(df.query$val.hits == 3) & (df.query$family == 'LTR/Copia')]))
```



```{r}

# one.te.fam = 'BRODYAGA1'
# one.te.fam = 'BRODYAGA2'
# one.te.fam = 'HELITRONY1D'
# one.te.fam = 'HELITRONY3'
one.te.fam = 'ATCOPIA41'
query.fam = df.query$query[df.query$subfam == one.te.fam]


one.te.fam = 'ATCOPIA41'
query.fam = df.query$query[df.query$subfam == one.te.fam]

res.nest.famp = res.nest[(res.nest$V1 %in% query.fam) | (res.nest$V8 %in% query.fam),]


idx = res.nest.famp$p1 >= sim.cutoff
edges = cbind(res.nest.famp$V1[idx], res.nest.famp$V8[idx])
idx = res.nest.famp$p8 >= sim.cutoff
edges = rbind(edges, cbind(res.nest.famp$V8[idx], res.nest.famp$V1[idx]))


te.enges.names = unique(c(edges[,1], edges[,2]))
te.enges.fam = sapply(te.enges.names, function(s) strsplit(s, '\\|')[[1]][9] )
te.enges.fam[te.enges.fam %in% c('DNA/Pogo', 'DNA/Tc1', 'DNA/Harbinger', 'DNA/En-Spm',
                     'DNA/HAT', 'DNA', 'DNA/Mariner')] = 'DNA'
te.enges.fam[te.enges.fam %in% c('RathE1_cons', 'RathE2_cons', 'RathE3_cons')] = 'RathE1/2/3_cons'
te.enges.fam[te.enges.fam %in% c('LINE/L1', 'LINE?')] = 'LINE'
te.enges.fam[te.enges.fam %in% c('Unassigned')] = 'Mix'
te.enges.fam[te.enges.fam %in% c('RC/Helitron')] = 'Helitron'

g.part <- network(edges, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)
b.graph.len = as.numeric(sapply(strsplit(b.graph.names, "\\|"), function(x) x[5]))


label.family = sapply(strsplit(b.graph.names, "\\|"), function(x) x[8])
lab.cols = c('#3F2E3E', "white")
label.color = lab.cols[(label.family == one.te.fam) + 1]

set.seed(20)
p <- ggnet2(g.part, label = b.graph.len, edge.color = "black", 
             node.size = 15,
            alpha=0.8,
            arrow.gap = 0.015,
            arrow.size = 5,
            label.color = label.color,
            # node.size = g.nodes.cnt[b.graph.names], 
            color = te.enges.fam[b.graph.names],
            palette = fam.palette,
            # mode = "kamadakawai"
            ) + guides(size = F)
p 

pdf(paste(path.figures, 'real_tes_subfam_', one.te.fam, '.pdf', sep = ''), width = 20, height = 18)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

set.seed(20)
p <- ggnet2(g.part, label = b.graph.names, edge.color = "black",
             node.size = 15,
            alpha=0.8,
            arrow.gap = 0.015,
            arrow.size = 5,
            # label.color = label.color,
            # node.size = g.nodes.cnt[b.graph.names],
            color = te.enges.fam[b.graph.names],
            palette = fam.palette,
            # mode = "kamadakawai"
            ) + guides(size = F)

pdf(paste(path.figures, 'real_tes_subfam_', one.te.fam, '_names.pdf', sep = ''), width = 50, height = 49)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

```


## Dotplots

### Read TE sequences
```{r}
file.te.fasta = paste(path.tair, 'new_te.fasta', sep = '')
te.fasta = seqinr::read.fasta(file.te.fasta)
te.names = names(te.fasta)
te.fasta = seqinr::getSequence(te.fasta)
names(te.fasta) = te.names
```

### One pairwise example
```{r}
wsize = 10
nmatch = 8

seq1 = te.fasta[[b.graph.names[1]]]
seq2 = te.fasta[[b.graph.names[1]]]


name1 = 'te|12384763|12385262|4|500|+|AT4TE57580|BRODYAGA1A|DNA/MuDR'
name2 = 'te|13674917|13675271|1|355|+|AT1TE44760|BRODYAGA1|DNA/MuDR'

# name1 = 'te|10592111|10592664|1|554|-|AT1TE34265|BRODYAGA2|DNA/MuDR'
# name2 = 'te|8743238|8744263|4|1026|-|AT4TE39045|HELITRONY1D|RC/Helitron'

name1 = 'te|6283198|6284421|4|1224|-|AT4TE26710|ATREP15|RC/Helitron'
name2 = 'te|6283198|6284421|4|1224|-|AT4TE26710|ATREP15|RC/Helitron'

seq1 = te.fasta[[name1]]
seq2 = te.fasta[[name2]]

p = dotplot(seq1, seq2, wsize, nmatch)

p = p + annotate("text", x = -Inf, y = Inf, label = paste('wsize=',wsize,'\nnmatch=',nmatch, sep = ''), 
             hjust = -0.1, vjust = 1.1)

p

pdf(paste(path.figures, 'pairwise_','.pdf', sep = ''), width = 5, height = 5)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()


```

### one VS all
```{r}

wsize = 10
nmatch = 8

name0 = 'te|11683565|11689821|3|6257|+|AT3TE48540|ATCOPIA95|LTR/Copia'
name0 = 'te|16691748|16695154|1|3407|−|AT1TE55070|ATCOPIA41|LTR/Copia'
name0 = gsub('−', "-", name0)


one.te.fam = strsplit(name0, '\\|')[[1]][8]
# one.te.fam = 'BRODYAGA2'
query.fam = df.query$query[df.query$subfam == one.te.fam]
query.fam = query.fam[(query.fam %in% res.nest.sim$V1) | (query.fam %in% res.nest.sim$V2)]

names.all = setdiff(query.fam, name0)

p.all = list()
for(name2 in names.all){
  # message(name2)
  seq1 = te.fasta[[name0]]
  seq2 = te.fasta[[name2]]
  
  s1 = strsplit(name0, '\\|')[[1]][7]
  s2 = strsplit(name2, '\\|')[[1]][7]
  p = dotplot(seq1, seq2, wsize, nmatch) + xlab(s1) + ylab(s2)
  p.all[[name2]] = p
}
# 
# pp = grid.arrange(grobs = p.all, ncol = 13) ## display plot
# 
# 
# pdf(paste(path.figures, 'pairwise_all','.pdf', sep = ''), width = 50, height = 50)
# print(pp)     # Plot 1 --> in the first page of PDF
# dev.off()

s0 = paste0(strsplit(name0, '\\|')[[1]][7:9], collapse = '_')
s0 = gsub("/", "-", s0)
pdf(paste(path.figures, 'pairwise_all_',s0,'.pdf', sep = ''), width = 50, height = 50)
grid.arrange(grobs = p.all, ncol = ceiling(sqrt(length(p.all)))) # Write the grid.arrange in the file
dev.off() # Close the file

```


### one connected component
```{r}

wsize = 10
nmatch = 8


name0 = 'te|6205621|6206184|2|564|−|AT2TE25255|HELITRONY1D|RC/Helitron'
name0 = 'te|14189256|14190266|5|1011|-|AT5TE50700|HELITRONY3|RC/Helitron'
name0 = 'te|12513239|12513824|1|586|+|AT1TE40725|ATHILA4A|LTR/Gypsy'
name0 = 'te|11647426|11648912|1|1487|+|AT1TE37705|ATREP7|RC/Helitron'
name0 = 'te|11683565|11689821|3|6257|+|AT3TE48540|ATCOPIA95|LTR/Copia'
name0 = gsub('−', "-", name0)


names.all = unique(c(res.nest.sim$V1[res.nest.sim$V8 == name0],
                     res.nest.sim$V8[res.nest.sim$V1 == name0]))
# names.all = unique(c(res.nest$V1[res.nest$V8 == name0], 
#                      res.nest$V8[res.nest$V1 == name0]))

p.all = list()
for(name2 in names.all){
  # message(name2)
  seq1 = te.fasta[[name0]]
  seq2 = te.fasta[[name2]]
  
  s1 = paste0(strsplit(name0, '\\|')[[1]][7:9], collapse = '|')
  s2 = paste0(strsplit(name2, '\\|')[[1]][7:9], collapse = '|')
  p = dotplot(seq1, seq2, wsize, nmatch) + xlab(s1) + ylab(s2)
  p.all[[name2]] = p
}


s0 = paste0(strsplit(name0, '\\|')[[1]][7:9], collapse = '_')
s0 = gsub("/", "-", s0)
pdf(paste(path.figures, 'pairwise_connect_',s0,'.pdf', sep = ''), width = 50, height = 50)
grid.arrange(grobs = p.all, ncol = ceiling(sqrt(length(p.all)))) # Write the grid.arrange in the file
dev.off() # Close the file

```



```{r}


name1 = 'te|14189256|14190266|5|1011|-|AT5TE50700|HELITRONY3|RC/Helitron'
name2 = 'te|2162295|2162937|2|643|-|AT2TE09950|HELITRONY3|RC/Helitron'
name0 = name1

names.all = unique(c(res.nest$V1[res.nest$V8 == name0], res.nest$V8[res.nest$V1 == name0]))


names = c(name1, name2)
b.tmp = bl.res[(bl.res$V1 %in% names) & (bl.res$V8 %in% names),]

res.nest[(res.nest$V1 %in% names) & (res.nest$V8 %in% names), ]

```


# SVs
## Readings seSVs
```{r}

sv.se = readRDS(paste(path.svs, 'sv_se.rds', sep = ''))

# Rename length groups
lev.replace = c('[1,10]', '(10,15]')
lev.new = '[1,15]'

s.levels = as.character(levels(sv.se$len.gr))
s.levels = s.levels[!(s.levels %in% lev.replace)]
s.levels = c(lev.new, s.levels)
s.levels = gsub("e\\+03", "k", s.levels)

sv.se$len.gr = as.character(sv.se$len.gr)
sv.se$len.gr[sv.se$len.gr %in% lev.replace] = lev.new
sv.se$len.gr = gsub("e\\+03", "k", sv.se$len.gr)
sv.se$len.gr = factor(sv.se$len.gr, levels = s.levels)


# Replace families
sv.se$fam = as.character(sv.se$fam)
sv.se$fam <- gsub("Helitron/.*", "Mix with Helitron", sv.se$fam)


sv.se$te = factor(sv.se$te, levels = c('isTE', 'isTEpart', 'hasTE', 'hasTEpart', 'noTE'))



```

## Read SV fasta
```{r}

sv.seqs = seqinr::read.fasta(paste(path.svs, 'sv_pangen_seq_sv_big.fasta', sep = ''))

```


## Reading nestedness
```{r}

# Load similarity function

file.nestedness = paste(path.work, 'sv_big_on_big_nest.rds', sep = '')


if(!file.exists(file.nestedness)){
  bl.file = paste(path.work, 'sv_big_on_big.txt', sep = '')
  bl.res = read.table(bl.file)
  bl.res = bl.res[bl.res$V1 != bl.res$V8,]
  
  bl.res = bl.res[bl.res$V6 >= sim.cutoff * 100,]

  res.nest = findNestedness(bl.res, use.strand = F)
    
  res.nest$len1 = res.nest.len[res.nest$V1]
  res.nest$len8 = res.nest.len[res.nest$V8]
  res.nest$p1 = res.nest$C1 / res.nest$len1
  res.nest$p8 = res.nest$C8 / res.nest$len8  
  saveRDS(res.nest, file.nestedness, compress = F)
} else {
  res.nest = readRDS(file.nestedness)
}

res.nest.len = sapply(unique(c(res.nest$V1, res.nest$V8)), 
                      function(s) as.numeric(strsplit(s, '\\|')[[1]][2]))
res.nest0 = res.nest


# For further analysis of examples:
bl.file = paste(path.work, 'sv_big_on_big.txt', sep = '')
bl.res = read.table(bl.file)
bl.res = bl.res[bl.res$V1 != bl.res$V8,]

```


## TE stat
### In graph - not in graph
```{r}
res.nest = res.nest0

sv.se.len = sv.se[sv.se$len >= 100,]
sv.se.len$in.connect = sv.se.len$name %in% names(res.nest.len)

cnt.sv.se = table(sv.se.len$in.connect , sv.se.len$te)
cnt.sv.se

df = reshape2::melt(cnt.sv.se)



df$Var2 = factor(df$Var2, levels = rev(c('isTE', 'isTEpart', 'hasTE', 'hasTEpart', 'noTE')))


# install.packages("ggpattern")


p = ggplot(df, aes(x = Var2, y = value, fill = Var2, alpha = Var1, color = Var1)) +
  geom_col_pattern( aes(pattern = Var1),
    # pattern = rep(c('none', "stripe"), 5),
    pattern_density = 0.1,
    pattern_spacing = 0.025,
    pattern_fill = "grey70", 
    position = "dodge", 
    width = 0.8
  ) + 
  # geom_col(position = "dodge", width = 0.8) +
  scale_alpha_manual(values = c(0.8, 1), labels = c("No", "Yes")) +
  scale_color_manual(values = c('black', 'black'), labels = c("not in graph", "in graph")) +
  scale_pattern_manual(values = c("stripe", 'none'), labels = c("in graph", "not in graph"),
                       breaks = c(TRUE, FALSE)) +
  labs(fill = "", pattern='Connected to others') +
  scale_fill_manual(values = te.cols) +
  xlab(NULL) +
  ylab("Number of SVs") +
  theme(axis.text.y = element_blank()) + 
  guides(alpha = "none", fill = 'none', color = 'none') +
  theme_minimal() + coord_flip() +
  theme(
    legend.position = c(0.7, 0.3),     # Adjust these coordinates as needed
    legend.background = element_rect(fill="transparent", color='grey70')  
  ) +
  theme(axis.text.y = element_blank()) +
  guides(pattern = guide_legend(override.aes = list(fill = c("white"), color= 'black')))  
p

pdf(paste(path.figures, 'graph_mob_in_graph.pdf', sep = ''), width = 3, height = 4)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

```



### TE families in SV types
```{r}

sv.se.len = sv.se[sv.se$len >= 100,]
cnt.fam.sv = table(sv.se.len$fam[sv.se.len$fam!=''], sv.se.len$te[sv.se.len$fam!=''])
cnt.fam.sv = t(apply(cnt.fam.sv, 1, function(x) x/sum(x)))
cnt.fam.sv = cnt.fam.sv[, colSums(cnt.fam.sv) != 0]
cnt.fam.sv = reshape2::melt(cnt.fam.sv)

p = ggplot(cnt.fam.sv, aes(x = Var2, y = Var1, color = Var2)) + 
  geom_point(aes(size = value, alpha = value * 2)) + theme_minimal() + 
  scale_color_manual(values = te.cols)  +
  geom_text(data = cnt.fam.sv[cnt.fam.sv$value >= 0.2,], 
              aes(x=Var2, y=Var1, label = round(value, 2)), 
              size = 2.5, color = 'black', 
            nudge_x = 0.3,
            nudge_y = 0) +
  guides(size = "none", alpha = "none", color = 'none') +
  xlab('SV type') + ylab('TE family')
p


cnt.fam.sv = rowSums(table(sv.se.len$fam[sv.se.len$fam!=''], sv.se.len$te[sv.se.len$fam!='']))
cnt.fam.sv = data.frame(value = cnt.fam.sv, names = names(cnt.fam.sv))
rownames(cnt.fam.sv) = NULL

g = ggplot(cnt.fam.sv, aes(x = names, y = value)) +
  geom_bar(stat="identity", fill="grey80")+
  coord_flip() + theme_minimal() + theme(axis.title.y = element_blank(),
                        axis.text.y = element_blank(),
                        axis.ticks.y = element_blank()) +
  scale_y_continuous(labels = paste("1e",seq(0,4,1), sep = ''), breaks= seq(0,4,1)*1000) +
  ylab('#') + geom_text(aes(label=value, y=0), hjust=0, size = 2.5)
g 


pp = ggpubr::ggarrange(p + xlab('TE content') + scale_x_discrete(labels = c('is compl.', 'is fragm.', 
                               'cont. compl.', 'cont. fragm.')) , g, ncol = 2, widths = c(0.75, 0.25))
pp

pdf(paste(path.figures, 'graph_mob_te_fam_sv_type.pdf', sep = ''), width = 6, height = 4)
print(pp)     # Plot 1 --> in the first page of PDF
dev.off()


# Insertion and deletion
idx = (sv.se.len$fam!='') & (sv.se.len$freq.max <= 3)
table(sv.se.len$fam[idx], sv.se.len$te[idx])

idx = (sv.se.len$fam!='') & (sv.se.len$freq.max >= 25) & (sv.se.len$len >= 100) 
table(sv.se.len$fam[idx], sv.se.len$te[idx])
```
### TE fam: TAIR10
```{r}
f.te.ref = paste(path.tair, 'new_te.fasta', sep = '')
lines = readLines(f.te.ref)
lines = grep('^>', lines, value = T)

ref.fam = sapply(lines, function(x) strsplit(x, '\\|')[[1]][9])


indices <- grep("^DNA(?!/HAT|/MuDR)", ref.fam, value = FALSE, perl = TRUE)
ref.fam[indices] = 'DNA+'

indices <- grep("^RathE", ref.fam, value = FALSE, perl = TRUE)
ref.fam[indices] = 'RathE1/2/3_cons'

indices <- grep("^LINE", ref.fam, value = FALSE, perl = TRUE)
ref.fam[indices] = 'LINE'
ref.fam[ref.fam == 'RC/Helitron'] = 'Helitron'

ref.fam.cnt = table(ref.fam)



df = cnt.fam.sv
df$ref = as.numeric(ref.fam.cnt[df$names])
df = df[!is.na(df$ref),]

plot(df$value, df$ref)



p <- ggplot(df, aes(x = ref, y = value, color = names)) +
  geom_smooth(aes(group = 1), method = "lm", formula = y ~ x, se = FALSE, color = 'grey70') + 
  geom_point() +
  ggrepel::geom_text_repel(aes(label = names), max.overlaps = 20) +
  # xlab("log # in TAIR10 annotation") +
  # ylab("log # in SVs") +
  # scale_x_log10() +
  # scale_y_log10() +
  xlab("# in TAIR10 annotation") +
  ylab("# in SVs") +
  theme(legend.position = "none") +
  guides(color = FALSE) +
  theme_minimal()
p


lm_model <- lm(value ~ ref, data = df)
slope <- coef(lm_model)[2]


p = p + annotate("text", x = min(df$ref), y = max(df$value), 
           label = paste('Slope:', round(slope, 3)), hjust = 0, vjust = 1)



pdf(paste(path.figures, 'graph_mob_te_fam_tair10.pdf', sep = ''), width = 4, height = 4)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()


```


## Graph
### Filtration
```{r}

res.nest = res.nest0

sv.names.mix = sv.se$name[grep("^Mix", sv.se$fam)]
res.nest = res.nest[!(res.nest$V1 %in% sv.names.mix),]
res.nest = res.nest[!(res.nest$V8 %in% sv.names.mix),]


sv.names.mix = sv.se$name[sv.se$te == 'noTE']
res.nest = res.nest[!(res.nest$V1 %in% sv.names.mix),]
res.nest = res.nest[!(res.nest$V8 %in% sv.names.mix),]

singleton.mode = F
if(singleton.mode){
  sv.names.freq = sv.se$name[sv.se$freq.max <= 3]
  # sv.names.freq = sv.se$name[sv.se$freq.max >= 25]
  res.nest = res.nest[res.nest$V1 %in% sv.names.freq,]
  res.nest = res.nest[res.nest$V8 %in% sv.names.freq,]
}

prefix.mode = c('', '_single')


```


### Construct
```{r}
# all edges
idx = res.nest$p1 >= sim.cutoff
edges = cbind(res.nest$V1[idx], res.nest$V8[idx])
idx = res.nest$p8 >= sim.cutoff
edges = rbind(edges, cbind(res.nest$V8[idx], res.nest$V1[idx]))
te.enges.names = unique(c(edges[,1], edges[,2]))

tmp = sv.se$te
names(tmp) = sv.se$name
te.enges.type = as.character(tmp[te.enges.names])
names(te.enges.type) <- names(tmp[te.enges.names])


tmp = sv.se$fam
names(tmp) = sv.se$name
te.enges.fam = tmp[te.enges.names]

# nodes
idx = (res.nest$p1 >= sim.cutoff) & (res.nest$p8 >= sim.cutoff)
te.nodes = cbind(res.nest$V1[idx], res.nest$V8[idx])
te.rest = setdiff(te.enges.names, c(te.nodes[,1], te.nodes[,2]))


te.nodes.graph <- igraph::make_graph(t(te.nodes), directed = T)
te.nodes.graph <- igraph::simplify(te.nodes.graph)
te.nodes.comp <- igraph::components(te.nodes.graph)

nodes = data.frame(node = paste('N', te.nodes.comp$membership, sep = ''), te = names(te.nodes.comp$membership))

nodes.rest = data.frame(node = paste('R', (1:length(te.rest)), sep = ''), te = te.rest)
nodes = rbind(nodes, nodes.rest)

rownames(nodes) = nodes$te

# Define TE type
nodes.cnt = data.frame(cnt = c(table(nodes$node)))
nodes.cnt$node = rownames(nodes.cnt)
nodes.cnt$type = sapply(nodes.cnt$node, function(s){
  s.te = nodes$te[nodes$node == s]
  type.te = unique(te.enges.type[s.te])
  if(length(type.te) == 1){
    return(type.te)
  } else {
    type.te = table(type.te)
    type.te = names(type.te)[type.te == max(type.te)]
    return(type.te[1])
  }
})
table(nodes.cnt$type)

# Define TE family
nodes.cnt$fam = sapply(nodes.cnt$node, function(s){
  s.te = nodes$te[nodes$node == s]
  type.te = unique(te.enges.fam[s.te])
  if(length(type.te) == 1){
    return(type.te)
  } else {
    type.te = table(type.te)
    type.te = names(type.te)[type.te == max(type.te)]
    return(type.te[1])
  }
})
table(nodes.cnt$fam)


# Redefine edges but with node names
idx.endes = (edges[,1] %in% nodes$te) & (edges[,2] %in% nodes$te)
b.graph = cbind(nodes[edges[idx.endes,1], 'node'],nodes[edges[idx.endes,2], 'node'])
b.graph = unique(b.graph)
# b.graph = b.graph[b.graph[,1] != b.graph[,2],]
b.graph.uni = b.graph[b.graph[,1] == b.graph[,2],]
b.graph = b.graph[b.graph[,1] != b.graph[,2],]

length(unique(c(b.graph[,1], b.graph[,2])))

# reduce indirect arrows
idx.remove = c()
for(i.edge in 1:nrow(b.graph)){
  if(i.edge %% 1000 == 0) print(i.edge)
  tmp.to = b.graph[b.graph[,1] == b.graph[i.edge,1],2]
  tmp.from = b.graph[b.graph[,2] == b.graph[i.edge,2],1]
  if(length(intersect(tmp.to, tmp.from)) > 0) idx.remove = c(idx.remove, i.edge)
}
idx.remove = unique(idx.remove)
b.graph = b.graph[-idx.remove,]
# b.graph = rbind(b.graph, b.graph.uni)

# Print graph

g.nodes.type = nodes.cnt$type
names(g.nodes.type) = nodes.cnt$node
g.nodes.cnt = nodes.cnt$cnt
names(g.nodes.cnt) = nodes.cnt$node
g.nodes.fam = nodes.cnt$fam
names(g.nodes.fam) = nodes.cnt$node


# g.cols.names = c("noTE", "isTE", "hasTE", "hasTEpart", "isTEpart")
# g.cols = c('#FFD966', '#EB455F', '#7B6079', '#3C8DAD', '#79B773')
# names(g.cols) = g.cols.names


g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)

set.seed(20)
p <- ggnet2(g.part, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names], 
            color = g.nodes.type[b.graph.names],
            # mode = 'kamadakawai',
            # arrow.gap = 0, 
            # arrow.size = 3,
            palette = te.cols) + guides(size = F)
p

# path.figures  = '/Volumes/Samsung_T5/vienn/work_te/'
pdf(paste(path.figures, 'graph_mob_all_cluster', prefix.mode[singleton.mode+1] ,'_type.pdf', sep = ''), 
    width = 4.6, height = 4.6)
print(p+ theme(legend.position = "none"))     # Plot 1 --> in the first page of PDF
dev.off()


# set.seed(20)
# p <- ggnet2(g.part, label = F, edge.color = "grey30", 
#             node.size = g.nodes.cnt[b.graph.names], 
#             color = c('TE', 'noTE')[(g.nodes.type[b.graph.names] == 'noTE')*1+1],
#             # mode = 'kamadakawai',
#             # arrow.gap = 0, 
#             # arrow.size = 3,
#             palette = c('noTE' = 'black', 'TE' = '#AEC3AE')) + guides(size = F)
# p
# 
# # path.figures  = '/Volumes/Samsung_T5/vienn/work_te/'
# pdf(paste(path.figures, 'graph_mob_all_cluster', prefix.mode[singleton.mode+1] ,'_type.pdf', sep = ''), 
#     width = 5, height = 5)
# print(p+ theme(legend.position = "none"))     # Plot 1 --> in the first page of PDF
# dev.off()

```


### Colored by TE family
```{r}

if(length(setdiff(g.nodes.fam, names(fam.palette)))!=0) stop('not all families are defined')

set.seed(20)
p <- ggnet2(g.part, label = F, edge.color = "grey20", 
            node.size = g.nodes.cnt[b.graph.names], 
            color = g.nodes.fam[b.graph.names],
            # mode = 'kamadakawai',
            # arrow.gap = 0, 
            # arrow.size = 3,
            palette = fam.palette) + guides(size = F)
p = p + theme(legend.text = element_text(size = 8), 
          legend.title = element_blank(),
          legend.key.size = unit(0.5, "cm")) + guides(color = guide_legend(ncol = 2))
p


pdf(paste(path.figures, 'graph_mob_all_cluster', prefix.mode[singleton.mode+1] ,'_family.pdf', sep = ''), 
    width = 4.6, height = 4.6)
print(p+ theme(legend.position = "none"))     # Plot 1 --> in the first page of PDF
dev.off()


pdf(paste(path.figures, 'graph_mob_all_cluster', prefix.mode[singleton.mode+1] ,'_family_legend.pdf', sep = ''), width = 7, height = 5)
print(p+ coord_fixed(ratio = 1))     # Plot 1 --> in the first page of PDF
dev.off()


```


### Node size distribution
```{r}
df = data.frame(node = unique(nodes$node))
df$size = g.nodes.cnt[df$node]
df$fam = g.nodes.fam[df$node]
df$type = g.nodes.type[df$node]

fam.palette

p = ggplot(df, aes(x = type, y = size, color=fam)) +
  geom_jitter(width = 0.2) +
  labs(x = "Type", y = "Size") + 
  scale_y_continuous(trans = "log2") +
  scale_color_manual(values = fam.palette)+
  theme_minimal() +
  guides(color = guide_legend(ncol = 2)) +
  labs(color = "TE family") + xlab('') + ylab('Node size (Number of similar SVs)')
p


pdf(paste(path.figures, 'graph_mob_size_distribution.pdf', sep = ''), width = 6.5, height = 4)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

```



### Separately visualise connected components
```{r}
tmp.graph <- igraph::make_graph(t(b.graph), directed = T)
tmp.graph <- igraph::simplify(tmp.graph)
tmp.comp <- igraph::components(tmp.graph)

tmp.cnt = table(tmp.comp$membership)
tmp.cnt = -sort(-tmp.cnt)
head(tmp.cnt)

k = 1
tmp.k = as.numeric(names(tmp.cnt)[k])
tmp.names = names(tmp.comp$membership)[tmp.comp$membership == tmp.k]
b.graph.sub = b.graph[(b.graph[,1] %in% tmp.names) & 
                        (b.graph[,2] %in% tmp.names),]

g.part.sub.big <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names.sub.big = network.vertex.names(g.part.sub.big)


set.seed(20)
p <- ggnet2(g.part.sub.big, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names.sub.big], 
            color = g.nodes.type[b.graph.names.sub.big],
            mode = 'kamadakawai',
            palette = te.cols) + guides(size = F)
p.big.type = p + theme(legend.position = "none")

set.seed(20)
p <- ggnet2(g.part.sub.big, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names.sub.big], 
            color = g.nodes.fam[b.graph.names.sub.big],
            mode = 'kamadakawai',
            palette = fam.palette) + guides(size = F)
p.big.color = p + theme(legend.position = "none")


tmp.k = as.numeric(names(tmp.cnt)[k])
tmp.names = names(tmp.comp$membership)[tmp.comp$membership != tmp.k]
b.graph.sub = b.graph[(b.graph[,1] %in% tmp.names) & 
                        (b.graph[,2] %in% tmp.names),]

g.part.sub.small <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names.sub.small = network.vertex.names(g.part.sub.small)


set.seed(20)
p <- ggnet2(g.part.sub.small, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names.sub.small], 
            color = g.nodes.type[b.graph.names.sub.small],
            # mode = 'kamadakawai',
            palette = te.cols) + guides(size = F)
p.small.type =p + theme(legend.position = "none")

set.seed(20)
p <- ggnet2(g.part.sub.small, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names.sub.small], 
            color = g.nodes.fam[b.graph.names.sub.small],
            # mode = 'kamadakawai',
            palette = fam.palette) + guides(size = F)
p.small.color = p + theme(legend.position = "none")



```

#### Save
```{r}
# p.big.type
# p.big.color
# p.small.type
# p.small.color

size.units = 4.6

pdf(paste(path.figures, 'graph_mob_big_cluster', prefix.mode[singleton.mode+1] ,'_type.pdf', sep = ''), 
    width = size.units, height = size.units)
print(p.big.type)     # Plot 1 --> in the first page of PDF
dev.off()

pdf(paste(path.figures, 'graph_mob_big_cluster', prefix.mode[singleton.mode+1] ,'_family.pdf', sep = ''), 
    width = size.units, height = size.units)
print(p.big.color)     # Plot 1 --> in the first page of PDF
dev.off()

pdf(paste(path.figures, 'graph_mob_small_cluster', prefix.mode[singleton.mode+1] ,'_type.pdf', sep = ''), 
    width = 6, height = 6)
print(p.small.type)     # Plot 1 --> in the first page of PDF
dev.off()

pdf(paste(path.figures, 'graph_mob_small_cluster', prefix.mode[singleton.mode+1] ,'_family.pdf', sep = ''), 
    width = 6, height = 6)
print(p.small.color)     # Plot 1 --> in the first page of PDF
dev.off()



```


## Examples with big "has TE"
```{r}

path.components = paste(path.figures, 'components/', sep = '')
if (!dir.exists(path.components)) {
  dir.create(path.components)
}

# Find a big node, whish is "Have te", which which is not in the biggest connected component

nodes.havete.big = nodes.cnt$node[(nodes.cnt$cnt >= 7) & ((nodes.cnt$type == 'hasTE') | (nodes.cnt$type == 'hasTEpart'))]
nodes.havete.big = nodes.havete.big[nodes.havete.big %in% names(tmp.comp$membership)]


nodes.target = unique(nodes.havete.big[tmp.comp$membership[nodes.havete.big] != as.numeric(names(tmp.cnt)[1])])

# for(i.target in 1:min(80, length(nodes.target))){
for(i.target in c(1)){
  message(i.target)
  
  comp.target = as.numeric(tmp.comp$membership[nodes.target[i.target]])
  
  # Visualise one component
  
  tmp.k = comp.target
  tmp.names = names(tmp.comp$membership)[tmp.comp$membership == tmp.k]
  b.graph.sub = b.graph[(b.graph[,1] %in% tmp.names) | 
                          (b.graph[,2] %in% tmp.names),,drop=F]
  
  g.part.sub.big <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
  b.graph.names.sub.big = network.vertex.names(g.part.sub.big)
  
  
  set.seed(20)
  p.te <- ggnet2(g.part.sub.big, label = F, edge.color = "black", 
              node.size = g.nodes.cnt[b.graph.names.sub.big], 
              color = g.nodes.type[b.graph.names.sub.big],
              mode = 'kamadakawai',
              arrow.gap = 0.04, arrow.size = 5,
              palette = te.cols) + guides(size = F)+ theme(legend.position = "none")
  
  
  set.seed(20)
  p.fam <- ggnet2(g.part.sub.big, label = g.nodes.cnt[b.graph.names.sub.big], edge.color = "black", 
              node.size = g.nodes.cnt[b.graph.names.sub.big],
              # node.size = 10, 
              color = g.nodes.fam[b.graph.names.sub.big],
              mode = 'kamadakawai',
              arrow.gap = 0.04, arrow.size = 5,
              palette = fam.palette) + guides(size = F)+ theme(legend.position = "none")
  
  pp = ggarrange(p.te, p.fam, nrow=1)
  
  # pp
  
  # TODO: third plot - frequency plot (three of colors: insertion, deletion, indel)
  
  pdf(paste(path.components, 'graph_mob_component_', sprintf("%03d", i.target),'.pdf', sep = ''), 
      width = 6, height = 3)
  print(pp)     # Plot 1 --> in the first page of PDF
  dev.off()

}



```
### Get sequences and align
```{r}

path.mafft = paste(path.work, 'mafft/', sep = '')
if (!dir.exists(path.mafft)) {
  dir.create(path.mafft)
}
  
sv.name.target = nodes$te[nodes$node %in% tmp.names]

seqs.target = sv.seqs[sv.name.target]
seqs.target.len = as.numeric(sapply(names(seqs.target), function(s) strsplit(s, '\\|')[[1]][2]))
seqs.target = seqs.target[order(-seqs.target.len)]
sv.name.target = names(seqs.target)

# Find orientations of sequences


bl.target = bl.res[(bl.res$V1 %in% sv.name.target) & (bl.res$V8 %in% sv.name.target),]
orientation.target = rep('.', length(seqs.target))
names(orientation.target) = sv.name.target
orientation.target[1] = '+'

# TODO: define the first orientation by the longest ORF

dir.seq = c('-', '+')

for(i in 1:(length(orientation.target) - 1)){
  bl.tmp = bl.target[bl.target$V1 == sv.name.target[i],]
  bl.tmp = bl.tmp[order(-bl.tmp$V7),]
  bl.tmp = bl.tmp[!duplicated(bl.tmp$V8),]
  bl.tmp$dir = bl.tmp$V5 > bl.tmp$V4
  bl.tmp = bl.tmp[orientation.target[bl.tmp$V8] == '.',]
  if(nrow(bl.tmp) == 0) next
  
  if(orientation.target[i] == '+'){
    orientation.target[bl.tmp$V8] = dir.seq[1 + (bl.tmp$dir) * 1]
  }
  
  if(orientation.target[i] == '-'){
    orientation.target[bl.tmp$V8] = dir.seq[2 - (bl.tmp$dir) * 1]
  }
  
  if(sum(orientation.target == '.') == 0) break
}
# 
for(i in which(orientation.target == '-')){
  seqs.target[[i]] = rev(seqinr::comp(seqs.target[[i]]))
}

seqs.target.msa = unlist(lapply(seqs.target, function(s) paste0(s, collapse = '')))
seqs.target.msa <- DNAStringSet(seqs.target.msa)

# alignment <- msa(seqs.target.msa)



# Run the alignment
tmp.fasta = paste(path.mafft, 'tmp.fasta', sep = '')
aln.fasta = paste(path.mafft, 'aln.fasta', sep = '')
writeXStringSet(seqs.target.msa, filepath = tmp.fasta)

system(paste('mafft --op 5 --quiet --maxiterate 100 ', tmp.fasta, '>', aln.fasta,  sep = ' '))

alignment = readDNAStringSet(aln.fasta)

seqs.mx = as.matrix(alignment)
msaplot(seqs.mx)

```

### Alignment step-by step
```{r}
# Align all sequences within each node

# sv.name.target = nodes$te[nodes$node %in% tmp.names]

aln.nodes = list()
seqs.all.cons = c()
for(s.node in tmp.names){
  sv.name.node = nodes$te[nodes$node %in% s.node]
  seqs.name.node = seqs.target.msa[sv.name.node]
  
  if(length(sv.name.node) == 1){
    seqs.mx = as.matrix(seqs.name.node)
    aln.nodes[[s.node]] = seqs.mx
    seqs.all.cons[s.node] = as.character(seqs.name.node)
    next
  }
  
  tmp.fasta = paste(path.mafft, 'tmp.fasta', sep = '')
  aln.fasta = paste(path.mafft, 'aln.fasta', sep = '')
  writeXStringSet(seqs.name.node, filepath = tmp.fasta)
  
  system(paste('mafft --op 5 --quiet --maxiterate 100 ', tmp.fasta, '>', aln.fasta,  sep = ' '))
  
  alignment = readDNAStringSet(aln.fasta)[sv.name.node]
  seqs.mx = as.matrix(alignment)
  # msaplot(seqs.mx)
  
  # add consensus sequence

  cons.prof = seqinr::consensus(seqs.mx, method = 'profile')
  cons.prof = cons.prof[!(rownames(cons.prof) == '-'),]
  max.indexes <- apply(cons.prof, 2, which.max)
  cons.seq = rownames(cons.prof)[max.indexes]
  # seqs.mx = rbind(seqs.mx, cons.seq)
  
  aln.nodes[[s.node]] = seqs.mx
  seqs.all.cons[s.node] = paste0(cons.seq, collapse = '')
}

# Combine all seqs for the alignment
 
# Align consensuses
seqs.all.cons <- DNAStringSet(seqs.all.cons)

# Run the alignment
tmp.fasta = paste(path.mafft, 'tmp.fasta', sep = '')
aln.fasta = paste(path.mafft, 'aln.fasta', sep = '')
writeXStringSet(seqs.all.cons, filepath = tmp.fasta)

system(paste('mafft --op 5 --quiet --maxiterate 100 ', tmp.fasta, '>', aln.fasta,  sep = ' '))

alignment = readDNAStringSet(aln.fasta)

seqs.mx.cons = as.matrix(alignment)
msaplot(seqs.mx.cons)

# Combine consensuses back
seqs.mx.complete = c()
for(s.node in rownames(seqs.mx.cons)){
  aln.tmp = aln.nodes[[s.node]]
  mx.tmp = matrix('-', nrow = nrow(aln.tmp), ncol = ncol(seqs.mx.cons), dimnames = list(rownames(aln.tmp), NULL))
  mx.tmp[, seqs.mx.cons[s.node,] != '-'] = aln.tmp
  seqs.mx.complete = rbind(seqs.mx.complete, mx.tmp)
}

p.msa = msaplot(seqs.mx.complete[sv.name.target,])
# p.msa = msaplot(seqs.mx.complete)
p.msa


pdf(paste(path.components, 'graph_mob_component_', sprintf("%03d", i.target),'_msa.pdf', sep = ''), 
    width = 6, height = 4)
print(p.msa)     # Plot 1 --> in the first page of PDF
dev.off()

```
### Create graph from without node collapse
```{r}



```




```{r}
s = rownames(seqs.mx)

s1 = as.character(as.vector(seqs.target.msa[[1]]))
s2 = as.character(as.vector(seqs.target.msa[[4]]))
dotplot(s1, s2, 10, 9)

```


```{r}

s1 = as.character(as.vector(seqs.target.msa[['SVgr_1_id_122936|638']]))
s2 = as.character(as.vector(seqs.target.msa[["SVgr_5_id_136083|674"]]))
dotplot(s1, s2, 10, 8)


bl.target[(bl.target$V1 %in% s.tmp) & (bl.target$V8 %in% s.tmp), ]

```


```{r}

s1 = as.character(as.vector(seqs.target.msa[[1]]))
s2 = as.character(as.vector(seqs.target.msa[[4]]))
dotplot(s1, s2, 10, 8)


s2 = as.character(seqs.target.msa[[1]])
# todo: %ORF
```


# Stop
```{r}
stop('All graphs are created, Stop before the code for \"per accession\" ')
```



### Run by accessions
```{r}
path.figures.acc = '/Volumes/Samsung_T5/vienn/work_te/figures_tegraph_accessions/'
sv.bin = read.table('/Volumes/Samsung_T5/vienn/work_sv/svs_se_bin_v03.txt', stringsAsFactors = F, check.names = FALSE)
```


```{r}
# acc = '10002'

for(acc in colnames(sv.bin)){
  sv.acc = rownames(sv.bin)[sv.bin[,acc] == 1]
  rownames(sv.se) = sv.se$gr
  sv.acc = sv.se[sv.acc, 'name']
  
  sv.acc = intersect(sv.acc, rownames(nodes))
  nodes.cnt.acc = table(nodes[sv.acc,'node'])
  
  
  sv.alpha = rep(0, length(b.graph.names))
  names(sv.alpha) = b.graph.names
  sv.alpha[names(sv.alpha) %in% names(nodes.cnt.acc)] = 1
  
  # set.seed(239)
  # p <- ggnet2(g.part, label = F, edge.color = "black", 
  #             node.size = g.nodes.cnt[b.graph.names], 
  #             color = g.nodes.fam[b.graph.names],
  #             alpha = sv.alpha,
  #             # mode = 'kamadakawai',
  #             # arrow.gap = 0, 
  #             # arrow.size = 3,
  #             palette = fam.palette) + guides(size = F) + theme(legend.position = "none")
  
  set.seed(20)
  p <- ggnet2(g.part.sub.small, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names.sub.small], 
            color = g.nodes.fam[b.graph.names.sub.small],
            alpha = sv.alpha[b.graph.names.sub.small],
            # mode = 'kamadakawai',
            palette = fam.palette) + guides(size = F) + theme(legend.position = "none")

  pdf(paste(path.figures.acc, 'graph_te', prefix.mode[singleton.mode+1] ,'_small_acc_',acc,'.pdf', sep = ''), width = 5, height = 5)
  print(p)     # Plot 1 --> in the first page of PDF
  dev.off()
  
  
  set.seed(20)
  p <- ggnet2(g.part.sub.big, label = F, edge.color = "black", 
            node.size = g.nodes.cnt[b.graph.names.sub.big], 
            color = g.nodes.fam[b.graph.names.sub.big],
            alpha = sv.alpha[b.graph.names.sub.big],
            mode = 'kamadakawai',
            palette = fam.palette) + guides(size = F) + theme(legend.position = "none")

  pdf(paste(path.figures.acc, 'graph_te', prefix.mode[singleton.mode+1] ,'_big_acc_',acc,'.pdf', sep = ''), width = 5, height = 5)
  print(p)     # Plot 1 --> in the first page of PDF
  dev.off()

}

p 



```


```{r}
sv.annot = read.table('/Volumes/Samsung_T5/vienn/work_sv/svs_annotation_v03.txt', stringsAsFactors = F)
rownames(sv.annot) = sv.annot$gr
head(sv.annot)

sv.annot[extracted_values,]

```




# Big TE-nodes
```{r}
n.amount = 20

g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)

size.big = g.nodes.cnt[b.graph.names]
alpha.big = rep(1, length(b.graph.names))
names(alpha.big) = b.graph.names
alpha.big[size.big < n.amount] = 0

sum(size.big >= n.amount)

set.seed(20)
p <- ggnet2(g.part, label = F, edge.color = "black", 
            node.size = size.big, 
            color = g.nodes.fam[b.graph.names],
            alpha= alpha.big,
            # mode = 'kamadakawai',
            # arrow.gap = 0, 
            # arrow.size = 3,
            palette = fam.palette) + guides(size = F) + guides(color = guide_legend(ncol = 2))
p

pdf(paste(path.figures, 'graph_small_cluster', prefix.mode[singleton.mode+1] ,'_family_amount.pdf', sep = ''), width = 5, height = 5)
print(p+ theme(legend.position = "none"))     # Plot 1 --> in the first page of PDF
dev.off()





```

## Which families specifically, and is the rate of insertion is different?
compare number of insertions with the total number of TE load
```{r}

big.families = data.frame(node =  names(size.big)[size.big >= n.amount])
big.families$size = size.big[big.families$node]
big.families$fam = g.nodes.fam[big.families$node]
big.families = big.families[order(-big.families$size),]
rownames(big.families) = NULL

node.big = nodes[nodes$node %in% big.families$node,]

v = read.table(paste(path.work, 'blast_sv_on_tes.txt', sep = ''))
v = v[v$V1 %in% node.big$te,]


pos.len1 = 2
pos.len2 = 5
v1.len = sapply(unique(v$V1), function(s) as.numeric(strsplit(s,'\\|')[[1]][pos.len1]))
v8.len = sapply(unique(v$V8), function(s) as.numeric(strsplit(s,'\\|')[[1]][pos.len2]))
v.len = c(v1.len, v8.len)

v.sim = findNestedness(v, use.strand = F)

v.sim = findNestedness(v, use.strand = F)
v.sim$p1 = v.sim$C1 / v.len[v.sim$V1]
v.sim$p8 = v.sim$C8 / v.len[v.sim$V8]
v.sim$p1.in8 = v.sim$C1 / v.len[v.sim$V8]
v.sim$p8.in1 = v.sim$C8 / v.len[v.sim$V1]

node.big$subfam = ''
for(sv.name in unique(v.sim$V1)){
  v.tmp = v.sim[v.sim$V1 == sv.name,]
  s = v.tmp$V8[which.max(v.tmp$p1)]
  s = strsplit(s, '\\|')[[1]][8]
  node.big[sv.name, 'subfam'] = s
}


x = tapply(node.big$subfam, node.big$node, function(x){
  cnt = table(x)
  x = names(cnt)[cnt == max(cnt)]
  return(paste0(x, collapse =  ','))
})

big.families$subfam = x[big.families$node]


```



# no-TE SV
## Construct
```{r}
sv.se = readRDS(paste(path.svs, 'sv_se.rds', sep = ''))
sim.cutoff = 0.85


sv.se.no.te = sv.se$name[(sv.se$te == 'noTE') & (sv.se$len > 50)]

bl.file = paste(path.work,'sv_big_on_big.txt', sep = '')
bl.sv = read.table(bl.file, stringsAsFactors = F)
bl.sv = bl.sv[bl.sv$V1 != bl.sv$V8,]

# remove having TEs
bl.sv = bl.sv[bl.sv$V1 %in% sv.se.no.te, ]
bl.sv = bl.sv[bl.sv$V8 %in% sv.se.no.te, ]

pos.len1 = 2
sv.len = sapply(unique(c(bl.sv$V1, bl.sv$V8)), function(s) as.numeric(strsplit(s,'\\|')[[1]][pos.len1]))
bl.sv$len1 = sv.len[bl.sv$V1]
bl.sv$len8 = sv.len[bl.sv$V8]
max.len = 20000
bl.sv = bl.sv[(bl.sv$len1 <= max.len) & (bl.sv$len8 <= max.len),]
bl.sv$p1 = (bl.sv$V3 - bl.sv$V2 + 1) / bl.sv$len1
bl.sv$p8 = (abs(bl.sv$V5 - bl.sv$V4) + 1) / bl.sv$len8
bl.sv$comb = as.factor(paste(bl.sv$V1, bl.sv$V8, sep = '||'))

idx.mutual = (bl.sv$p1 >= sim.cutoff) & (bl.sv$p8 >= sim.cutoff)
# There is a big discussion in my head, whether it should be '&' or '|'
# If it's not ,utual, then maybe with something else it will construct a mutual relation, 
# so we should remain for the analysis of nestedness all partial inclusions
sv.mutual = bl.sv[idx.mutual, ]
v = bl.sv[!idx.mutual, ]
v = v[!(v$comb %in% sv.mutual$comb),]

# At some point it was a step to remain only those instances which are not "unique" in combinations
# but I think it's not correct here

sv.sim = findNestedness(v, use.strand = T)
sv.sim$p1 = sv.sim$C1 / sv.len[sv.sim$V1]
sv.sim$p8 = sv.sim$C8 / sv.len[sv.sim$V8]

# here  we should finally use '|', not '&'
sv.nested = sv.sim[(sv.sim$p1 >= sim.cutoff) | (sv.sim$p8 >= sim.cutoff) ,]

# Create pre-data for defining edges
common.names = intersect(colnames(sv.mutual), colnames(sv.nested))
sv.overall = rbind(sv.mutual[,common.names], sv.nested[,common.names])
sv.overall$group = (sv.overall$p1 >= sim.cutoff) * 1 + (sv.overall$p8 >= sim.cutoff) * 2
idx1 = sv.overall$group != 2  # V1 in V8
idx2 = sv.overall$group != 1  # V8 in V1


# Edges 
sv.edges = rbind(cbind(sv.overall$V1[idx1], sv.overall$V8[idx1]),
                 cbind(sv.overall$V8[idx2], sv.overall$V1[idx2]))


sv.graph <- igraph::make_graph(t(sv.edges), directed = T)
sv.graph <- igraph::simplify(sv.graph)
sv.graphcomp <- igraph::components(sv.graph)

sv.memb = data.frame(memb = sv.graphcomp$membership)
sv.memb$name = rownames(sv.memb)
rownames(sv.memb) = NULL
rownames(sv.se) = sv.se$name
sv.memb$te = sv.se[sv.memb$name, 'te']
sv.memb$cover = sv.se[sv.memb$name, 'cover'] / sv.se[sv.memb$name, 'len']
sv.memb$len = sv.len[sv.memb$name]
```

## Plot all
```{r}
g.part <- network(sv.edges, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
b.graph.names = network.vertex.names(g.part)

set.seed(239)
p <- ggnet2(g.part, label = F, edge.color = "black", 
            node.size = 1,
            # node.size = g.nodes.cnt[b.graph.names], 
            # color = g.nodes.type[b.graph.names],
            # palette = te.cols
            ) + guides(size = F)
p 

```

## Plot with colors
```{r}
sv.prot.init = readRDS(paste(path.work, 'sv_proteins_no_te_blast.rds', sep = ''))
sv.prot.init$name = sapply(sv.prot.init$X1, function(s){
  s = paste0(strsplit(s, '\\|')[[1]][1:2], collapse = '|')
  return(s)
})
sv.prot = sv.prot.init[sv.prot.init$prot == 1,]
sv.prot[,2] = tolower(sv.prot[,2])

types = c('disease', 'repeat', 'receptor',  'zinc', 'transcriptase', 'reverse', 'transpos')
for(i.type in 1:length(types)){
  sv.prot[,types[i.type]] = (grepl(types[i.type], sv.prot[,2])) * 1
}
sv.prot$type = rowSums(sv.prot[,types])
table(sv.prot$type)



sv.memb$prot = 'no prot'
sv.memb$prot[sv.memb$name %in% sv.prot.init$name] = 'undefined prot'
sv.memb$prot[sv.memb$name %in% sv.prot$name] = 'defined prot'
for(type in types){
  sv.memb$prot[sv.memb$name %in% sv.prot$name[sv.prot[,type] == 1]] = type
}

g.nodes.prot = sv.memb$prot
g.nodes.prot[g.nodes.prot == 'disease'] = 'defined prot'
names(g.nodes.prot) = sv.memb$name



set.seed(239)
p <- ggnet2(g.part, label = F, edge.color = "black", 
            # node.size = g.nodes.cnt[b.graph.names], 
            node.size = 1,
            color = g.nodes.prot[b.graph.names],
            palette = g.cols,
            # mode = "kamadakawai"
            ) + guides(size = F) + coord_fixed(ratio = 1) +
  scale_color_manual(values = g.cols, 
                       breaks = c("transpos","reverse",
                                  "repeat","zinc","receptor", "defined prot", "undefined prot",
                                  "no prot"), 
                     name = 'Protein key-word:') + theme(legend.justification = c(1, 0))
p = p+ theme(legend.key.height = unit(0.5, "cm"))
p

pdf(paste(path.figures, 'graph_new_all.pdf', sep = ''), width = 6, height = 4)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

# 
# cnt = table(g.nodes.prot)
# cnt = c(sum(cnt[c("transpos","reverse","repeat","zinc")]), sum(cnt[c("receptor","defined prot")]),
#         cnt["undefined prot"], cnt["no prot"])

```

## Types of the component


```{r}

sv.graph <- igraph::make_graph(t(sv.edges), directed = T)
sv.graph <- igraph::simplify(sv.graph)
sv.graphcomp <- igraph::components(sv.graph)

sv.comp.member = sv.graphcomp$membership

s.tags = c("transpos","reverse","repeat","zinc", "receptor","defined prot", "undefined prot", 'no prot')
s.tags0 = rep('', length(s.tags))
s.tags0[1:4] = 'TE-like'
s.tags0[5:6] = 'Known Proteins'
s.tags0[7] = 'Undef. Proteins'
s.tags0[8] = 'No Proteins'
names(s.tags0) = s.tags

comp.tags = rep('', length(unique(sv.comp.member)))
for(s.tag in s.tags){
  tmp.tags = unique(sv.comp.member[names(g.nodes.prot)[g.nodes.prot == s.tag]])
  comp.tags[tmp.tags][comp.tags[tmp.tags] == ''] = s.tag
}
comp.tags[comp.tags == ''] = 'no prot'
comp.tags = data.frame(table(comp.tags))
colnames(comp.tags) = c('tag1', 'freq')
comp.tags$tag1 = factor(comp.tags$tag1, levels = s.tags)
comp.tags = comp.tags[order(comp.tags$tag1),]

comp.tags$tag0 = s.tags0[comp.tags$tag1]
comp.tags$tag0 = factor(comp.tags$tag0, levels = unique(s.tags0))

y.ticks = tapply(comp.tags$freq, comp.tags$tag0, sum)
y.ticks = y.ticks[!is.na(y.ticks)]

yy = sum(y.ticks) - cumsum(y.ticks) + y.ticks/2

comp.tags$ymin <- c(0, cumsum(comp.tags$freq)[-length(comp.tags$freq)])
comp.tags$ymax <- cumsum(comp.tags$freq)

x.step = rep(0, 8)
n.step = 10
x.step[c(5,7,8)] = n.step
x.step = cumsum(x.step)

comp.tags$ymin = comp.tags$ymin + x.step
comp.tags$ymax = comp.tags$ymax + x.step

y.min = tapply(comp.tags$ymin, comp.tags$tag0, min)
y.max = tapply(comp.tags$ymax, comp.tags$tag0, max)
y.val = (y.max + y.min) / 2
y.cnt = tapply(comp.tags$freq, comp.tags$tag0, sum)

df.text = data.frame(y.min = y.min, y.max = y.max, y.val = y.val, y.cnt = y.cnt, label = names(y.val))
df.text$angles <- 360 - (df.text$y.val / (max(comp.tags$ymax) + n.step)) * 360 
df.text$angles[2:3] = 180 + df.text$angles[2:3]

p = ggplot(comp.tags, aes(x = 0, y = freq, fill = tag1)) +
   geom_rect(aes(xmin = -0.5, xmax = 0.5, ymin = ymin, ymax = ymax)) +
   coord_polar("y", start = 0) +
   scale_fill_manual(values = g.cols.plus) + ylim(0, max(comp.tags$ymax) + n.step) +
   theme_void() + xlim(-1.5, 0.7) + 
   geom_text(data=df.text, aes(x = 0.7, y = y.val, label = paste(label, y.cnt, sep = ': ')), 
             angle = df.text$angles, inherit.aes = FALSE) +
  theme(legend.position="none") + 
  annotate("text", x = -1.5, y = 0, label = paste('Total',sum(comp.tags$freq),'\n connected \ncomponents')) 

p

pdf(paste(path.figures, 'graph_new_pie_chart.pdf', sep = ''), width = 3.1, height = 3.1)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

```



#### I don't know
```{r}
sv.se$freq = sv.se$freq.max
n.cutoff = 3
n = 28
sv.se$sin = 'indel'
sv.se$sin[sv.se$freq >= (n - n.cutoff)] = 'deletion'
sv.se$sin[sv.se$freq <= n.cutoff] = 'insertion'


g.nodes.prot.sin = g.nodes.prot
g.nodes.prot.sin[names(g.nodes.prot.sin) %in% sv.se$name[sv.se$sin != 'insertion'] ] = 'na'
g.cols['na'] = 'white'




set.seed(239)
p <- ggnet2(g.part, label = F, edge.color = "black", 
            # node.size = g.nodes.cnt[b.graph.names], 
            node.size = 1,
            color = g.nodes.prot.sin[b.graph.names],
            palette = g.cols,
            # mode = "kamadakawai"
            ) + guides(size = F)
p 

# 
# path.figures  = '/Volumes/Samsung_T5/vienn/work_te/'
# pdf(paste(path.figures, 'graph_sv_note_insertion.pdf', sep = ''), width = 6, height = 4)
# print(p)     # Plot 1 --> in the first page of PDF
# dev.off()


alpha.edta = rep(1, length(b.graph.names))
names(alpha.edta) = b.graph.names

sv.annot.adta = rowSums(sv.annot[,11:ncol(sv.annot)] > 0.7) > 0
sv.annot.adta = sv.annot.adta[sv.se$gr]
names(sv.annot.adta) = sv.se$name
sv.annot.adta = sv.annot.adta[sv.annot.adta]
alpha.edta[names(alpha.edta) %in% names(sv.annot.adta)] = 0


set.seed(239)
p <- ggnet2(g.part, label = F, edge.color = "black", 
            # node.size = g.nodes.cnt[b.graph.names], 
            node.size = 1,
            alpha=1-alpha.edta,
            color = g.nodes.prot[b.graph.names],
            palette = g.cols,
            # mode = "kamadakawai"
            ) + guides(size = F)
p 


pdf(paste(path.figures, 'graph_mob_note_edta.pdf', sep = ''), width = 6, height = 4)
print(p)     # Plot 1 --> in the first page of PDF
dev.off()

path.figures  = '/Volumes/Samsung_T5/vienn/work_te/'
pdf(paste(path.figures, 'graph_mob_note_edta_no_legend.pdf', sep = ''), width = 5, height = 5)
print(p+ theme(legend.position = "none"))     # Plot 1 --> in the first page of PDF
dev.off()

```


## Plot with component ID
```{r}


tmp.graph <- igraph::make_graph(t(sv.edges), directed = T)
tmp.graph <- igraph::simplify(tmp.graph)
tmp.comp <- igraph::components(tmp.graph)

size.limit = 5
comp.id = as.character(tmp.comp$membership)
names(comp.id) = names(tmp.comp$membership)
comp.id[tmp.comp$csize[tmp.comp$membership] < size.limit] = ''

names.te = names(g.nodes.prot)[g.nodes.prot %in% c('transpos', 'reverse')]

comp.id[!(names(comp.id) %in% names.te)] = ''

comp.id[duplicated(comp.id)] = ''


comp.remain = as.numeric(comp.id[comp.id != ''])
alpha = rep(0, length(b.graph.names))
names(alpha) = names(tmp.comp$membership)
alpha[tmp.comp$membership %in% comp.remain] = 1

set.seed(239)
p <- ggnet2(g.part, label = comp.id[b.graph.names], 
            label.color = "black",
            label.size = 3,
            edge.color = "grey", 
            alpha = alpha[b.graph.names],
            # node.size = g.nodes.cnt[b.graph.names], 
            node.size = 1,
            color = g.nodes.prot[b.graph.names],
            palette = g.cols,
            # mode = "kamadakawai"
            ) + guides(size = F)
p 


path.figures  = '/Volumes/Samsung_T5/vienn/work_te/'
pdf(paste(path.figures, 'graph_sv_note_numbers.pdf', sep = ''), width = 5, height = 5)
print(p + theme(legend.position = "none"))     # Plot 1 --> in the first page of PDF
dev.off()



# Order of components
cnt = table(tmp.comp$membership[tmp.comp$membership %in% comp.remain])
cnt = cnt[order(-cnt)]

```

## CNV
```{r}

cnv = readRDS('/Volumes/Samsung_T5/vienn/work_sv/similar_cnv_sv_on_accessions_cum_0.9.rds')

```

## Plot one specific network
```{r}

path.figures.examples  = '/Volumes/Samsung_T5/vienn/work_te/examples/'

# 
# tmp.graph <- igraph::make_graph(t(sv.edges), directed = T)
# tmp.graph <- igraph::simplify(tmp.graph)
# tmp.comp <- igraph::components(tmp.graph)
# 
# tmp.cnt = table(tmp.comp$membership)
# tmp.cnt = -sort(-tmp.cnt)

tmp.cnt = cnt

for(k in 1:length(tmp.cnt)){
  tmp.k = as.numeric(names(tmp.cnt)[k])
  tmp.names = names(tmp.comp$membership)[tmp.comp$membership == tmp.k]
  b.graph.sub = sv.edges[(sv.edges[,1] %in% tmp.names) & 
                          (sv.edges[,2] %in% tmp.names),]
  
  
  g.part.sub <- network(b.graph.sub, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
  b.graph.names.sub = network.vertex.names(g.part.sub)
  
  
    
  b.graph.size.sub <- as.numeric(sub(".*\\|", "", b.graph.names.sub))
  names(b.graph.size.sub) = b.graph.names.sub
  # b.graph.size.sub = ceiling(log(b.graph.size.sub, 10))
  
  if((length(unique( g.nodes.prot[b.graph.names.sub])) == 1)){
    set.seed(20)
    p <- ggnet2(g.part.sub, label = b.graph.size.sub[b.graph.names.sub], edge.color = "black", 
                node.size = 15,
                arrow.gap = 0.07, arrow.size = 3,
                color = g.cols[g.nodes.prot[b.graph.names.sub][1]],
                ) + guides(size = F) +  ggtitle(paste('Component #', tmp.k))
    p
  } else {
    set.seed(20)
    p <- ggnet2(g.part.sub, label = b.graph.size.sub[b.graph.names.sub], edge.color = "black", 
                node.size = 15,
                arrow.gap = 0.07, arrow.size = 3,
                color = g.nodes.prot[b.graph.names.sub],
                palette = g.cols,
                ) + guides(size = F) +  ggtitle(paste('Component #', tmp.k))
    p
  }
  
 
  
  pdf(paste(path.figures.examples, 'graph_sv_example_',k,'_comp_',tmp.k,'.pdf', sep = ''), width = 5, height = 4)
  print(p + theme(legend.position = "none"))     # Plot 1 --> in the first page of PDF
  dev.off()
  
  # annotation
  annot.tmp = sv.prot[sv.prot$name %in% b.graph.names.sub,]
  # annot.tmp = annot.tmp[annot.tmp$transpos == 1,]
  
  write.table(annot.tmp, paste(path.figures.examples, 'graph_sv_example_',k,'_pblast.txt', sep = ''), 
              row.names = F, col.names = F, quote = F, sep = '\t')
  
  
  # if EDTA annotation exists
  sv.tmp = unique(c(b.graph.sub))
  sv.tmp.cut <- gsub("\\|.*", "", sv.tmp)
  sv.annot.tmp = sv.annot[sv.tmp.cut,]
  n.fix = 9
  sv.annot.tmp  = sv.annot.tmp[,c(1:n.fix,n.fix+which(colSums(sv.annot.tmp[,(n.fix+1):ncol(sv.annot.tmp)]) != 0))]
  rownames(sv.annot.tmp) = sv.tmp
    
  write.table(sv.annot.tmp, paste(path.figures.examples, 'graph_sv_example_',k,'_edta.txt', sep = ''), 
             row.names = F, quote = F, sep = '\t')
  
  # Copy0Number variation
  cnv.tmp = cnv[sv.tmp,]
  
  heatmap(cnv.tmp, col = colorRampPalette(c("white", "red"))(20))
  
}

```
# Pie-chart of proteins
```{r}
library(ggplot2)

data <- data.frame(
  type = c("no proteins", "TE-related", "Категория 2", "Категория 3", "Категория 4"),
  value = c(135, 63, 85, 133)
)

pie.chart <- ggplot(data, aes(x = "", y = value, fill = type)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0) +
  theme_void()

pie.chart

```

## Admixture groups
```{r}
groups <- c(
  "germany",
  "south_sweden",
  "north_sweden",
  "south_sweden",
  "north_sweden",
  "germany",
  "western_europe",
  "central_europe",
  "italy_balkan_caucasus",
  "spain",
  "relict",
  "asia",
  "central_europe",
  "admixed",
  "spain",
  "relict",
  "italy_balkan_caucasus",
  "western_europe",
  "asia",
  "africa",
  "china",
  "china",
  "africa",
  "africa",
  "madeira",
  "madeira",
  "africa"
)

# Используем функцию table() для подсчета количества элементов в каждой группе
as.matrix(table(groups))
```



# OLD
```{r}
sunset <- colour("sunset")
discrete_rainbow <- colour("discrete rainbow")

file.te = '/Volumes/Samsung_T5/vienn/work/blast_tes_ann.txt'
sim.cutoff = 0.85
len.cutoff = 100
```


```{r}

b = read.table(file.te, stringsAsFactors = F)
b = b[b$V1 != b$V8,]
b$len1 = as.numeric(sapply(b$V1, function(s) strsplit(s, '\\|')[[1]][7]))
b$len2 = as.numeric(sapply(b$V8, function(s) strsplit(s, '\\|')[[1]][7]))
b = b[b$len1 >= len.cutoff,]
b = b[b$len2 >= len.cutoff,]
b$comb = paste(b$V1, b$V8, sep = '^')

# Order positions in base
idx = b$V4 > b$V5
tmp = b[idx, 'V4']
b[idx, 'V4'] = b[idx, 'V5']
b[idx, 'V5'] = tmp

# --------------------------------------------------
# Get separately those, who has a unique coverage
comb.tbl = table(b$comb)
idx.uni = b$comb %in% names(comb.tbl)[comb.tbl == 1]
b.uni = b[idx.uni,]
b = b[!idx.uni,]

# This variable will be used later
b.uni$p1 = (b.uni$V3 - b.uni$V2 + 1) / b.uni$len1
b.uni$p2 = (b.uni$V5 - b.uni$V4 + 1) / b.uni$len2
b.uni = b.uni[(b.uni$p1 >= sim.cutoff) | (b.uni$p2 >= sim.cutoff),]

b.relations = data.frame(sub.te = b.uni$V1[b.uni$p1 >= sim.cutoff],
                         te = b.uni$V8[b.uni$p1 >= sim.cutoff], stringsAsFactors = F)
b.relations = rbind(b.relations,
                    data.frame(sub.te = b.uni$V8[b.uni$p2 >= sim.cutoff],
                               te = b.uni$V1[b.uni$p2 >= sim.cutoff], stringsAsFactors = F))
b.relations = unique(b.relations)

# --------------------------------------------------
# Min-max of the coverage to remove those, who are NOT in each other completely
b.cov = tapply(b$V2, b$comb, min)
b.cov = data.frame(comb = names(b.cov), V2 = b.cov)
b.cov$V3 = tapply(b$V3, b$comb, max)
b.cov$V4 = tapply(b$V4, b$comb, min)
b.cov$V5 = tapply(b$V5, b$comb, max)
b.cov$len1 = tapply(b$len1, b$comb, unique)
b.cov$len2 = tapply(b$len2, b$comb, unique)
b.cov$p1 = (b.cov$V3 - b.cov$V2 + 1) / b.cov$len1
b.cov$p2 = (b.cov$V5 - b.cov$V4 + 1) / b.cov$len2

comb.uncov = b.cov$comb[(b.cov$p1 < sim.cutoff) & (b.cov$p2 < sim.cutoff)]

b = b[!(b$comb %in% comb.uncov),]

# --------------------------------------------------
# Calculate the coverage directly for the first
b = b[order(b$V3),]
b = b[order(b$V2),]
b = b[order(b$comb),]

# Remove nested
idx = which((b$V3[-nrow(b)] > b$V3[-1]) & (b$comb[-nrow(b)] == b$comb[-1])) + 1
b1 = b[-idx,]

# Compute gaps
b1$gap = c(b1$V2[-1] - b1$V3[-nrow(b1)] - 1, 0)
b1$gap[b1$gap < 0] = 0
idx.diff.comb = which(b1$comb[-1] != b1$comb[-nrow(b1)])
b1$gap[idx.diff.comb] = 0

b.cov = tapply(b1$V2, b1$comb, min)
b.cov = data.frame(comb = names(b.cov), V2 = b.cov)
b.cov$V3 = tapply(b1$V3, b1$comb, max)
b.cov$len1 = tapply(b1$len1, b1$comb, unique)
b.cov$gap = tapply(b1$gap, b1$comb, sum)
b.cov$len1 = b.cov$len1 
b.cov$p1 = (b.cov$V3 - b.cov$V2 + 1 - b.cov$gap) / b.cov$len1
b.cov$V1 = tapply(b1$V1, b1$comb, unique)
b.cov$V8 = tapply(b1$V8, b1$comb, unique)

b.cov = b.cov[b.cov$p1 >= sim.cutoff,]


b.relations = rbind(b.relations,
                    data.frame(sub.te = b.cov$V1,
                               te = b.cov$V8, stringsAsFactors = F))


# --------------------------------------------------
# Calculate the coverage directly for the second
b = b[order(b$V5),]
b = b[order(b$V4),]
b = b[order(b$comb),]

# Remove nested
idx = which((b$V5[-nrow(b)] > b$V5[-1]) & (b$comb[-nrow(b)] == b$comb[-1])) + 1
b1 = b[-idx,]

# Compute gaps
b1$gap = c(b1$V4[-1] - b1$V5[-nrow(b1)] - 1, 0)
b1$gap[b1$gap < 0] = 0
idx.diff.comb = which(b1$comb[-1] != b1$comb[-nrow(b1)])
b1$gap[idx.diff.comb] = 0

b.cov = tapply(b1$V4, b1$comb, min)
b.cov = data.frame(comb = names(b.cov), V4 = b.cov)
b.cov$V5 = tapply(b1$V5, b1$comb, max)
b.cov$len2 = tapply(b1$len2, b1$comb, unique)
b.cov$gap = tapply(b1$gap, b1$comb, sum)
b.cov$len2 = b.cov$len2 
b.cov$p1 = (b.cov$V5 - b.cov$V4 + 1 - b.cov$gap) / b.cov$len2
b.cov$V1 = tapply(b1$V1, b1$comb, unique)
b.cov$V8 = tapply(b1$V8, b1$comb, unique)

b.cov = b.cov[b.cov$p1 >= sim.cutoff,]


b.relations = rbind(b.relations,
                    data.frame(sub.te = b.cov$V8,
                               te = b.cov$V1, stringsAsFactors = F))

  
b.relations = unique(b.relations)


b.relations

```


# Define clusters
```{r}
b.nodes = rbind(b.relations,
                    data.frame(sub.te = b.relations$te,
                               te = b.relations$sub.te))

b.nodes$comb = paste(b.nodes$sub.te, b.nodes$te, sep = '^')

comb.tbl = table(b.nodes$comb)
comb.back.and.foth = names(comb.tbl)[comb.tbl >= 2]
b.nodes = b.nodes[b.nodes$comb %in% comb.back.and.foth,]
b.nodes = unique(b.nodes[, c('sub.te', 'te')])


te.nodes <- igraph::make_graph(t(b.nodes), directed = T)
te.nodes <- igraph::simplify(te.nodes)
te.nodes.comp <- igraph::components(te.nodes)

nodes = paste('N', te.nodes.comp$membership, sep = '')
names(nodes) = names(te.nodes.comp$membership)
```

## Identify family for each node
```{r}

nodes.family = sapply(names(nodes), function(s) strsplit(s, '\\|')[[1]][6])

nodes.family.max = tapply(nodes.family, nodes, function(s){
  tbl = table(s)
  f = names(tbl)[tbl == max(tbl)]
  if(length(f) == 1){
    return(f)
  } else {
    return('Mix')
  }
})

nodes.family.max[nodes.family.max %in% c('DNA/Pogo', 'DNA/Tc1', 'DNA/Harbinger', 'DNA/En-Spm',
                     'DNA/HAT', 'DNA', 'DNA/Mariner')] = 'DNA'
nodes.family.max[nodes.family.max %in% c('RathE1_cons', 'RathE2_cons')] = 'DNA'
nodes.family.max[nodes.family.max %in% c('LINE/L1', 'LINE?')] = 'LINE'
nodes.family.max[nodes.family.max %in% c('Unassigned')] = 'Mix'
nodes.family.unique = unique(nodes.family.max)



```


## Graph without singletons
```{r}

b.graph.init = b.relations[(b.relations$sub.te %in% names(nodes)) & (b.relations$te %in% names(nodes)),]
b.graph = b.graph.init
b.graph = cbind(nodes[as.character(b.graph$sub.te)], nodes[as.character(b.graph$te)])
b.graph = unique(b.graph)


b.graph = b.graph[b.graph[,1] != b.graph[,2],]

# reduce indirect arrows
idx.remove = c()
for(i.edge in 1:nrow(b.graph)){
  if(i.edge %% 1000 == 0) print(i.edge)
  tmp.to = b.graph[b.graph[,1] == b.graph[i.edge,1],2]
  tmp.from = b.graph[b.graph[,2] == b.graph[i.edge,2],1]
  if(length(intersect(tmp.to, tmp.from)) > 0) idx.remove = c(idx.remove, i.edge)
}
idx.remove = unique(idx.remove)
b.graph = b.graph[-idx.remove,]


# te.graph <- igraph::make_graph(t(b.graph), directed = T)
# te.graph <- igraph::simplify(te.graph)
# te.graph.comp <- igraph::components(te.graph)


nodes.family.max.graph = nodes.family.max[names(nodes.family.max) %in% unique(c(b.graph[,1], b.graph[,2]))]

graph.cols = sunset(length(unique(nodes.family.max.graph)))

graph.cols = discrete_rainbow(length(unique(nodes.family.max.graph)))
names(graph.cols) = unique(nodes.family.max.graph)
g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
p <- ggnet2(g.part, label = FALSE, edge.color = "black", node.size = 1, 
            color = nodes.family.max.graph, palette = graph.cols,
            mode = "kamadakawai")# + guides(size = FALSE)
p

```
## Graph WITH singletons
```{r}


names.core = names(nodes.family.max.graph)

b.graph.init = b.relations
for(i in 1:2){
  b.graph.init[b.graph.init[,i] %in% names(nodes), i] = nodes[b.graph.init[b.graph.init[,i] %in% names(nodes), i]]
}

b.graph = unique(b.graph.init)
b.graph = b.graph[b.graph[,1] != b.graph[,2],]
b.graph = unique(b.graph)
# Verteces from the previous graph
b.graph = b.graph[(b.graph[,1] %in% names.core) | (b.graph[,2] %in% names.core),]


# reduce indirect arrows
idx.remove = c()
for(i.edge in 1:nrow(b.graph)){
  if(i.edge %% 1000 == 0) print(i.edge)
  tmp.to = b.graph[b.graph[,1] == b.graph[i.edge,1],2]
  tmp.from = b.graph[b.graph[,2] == b.graph[i.edge,2],1]
  if(length(intersect(tmp.to, tmp.from)) > 0) idx.remove = c(idx.remove, i.edge)
}
idx.remove = unique(idx.remove)
b.graph = b.graph[-idx.remove,]

te.graph <- igraph::make_graph(t(b.graph), directed = T)
d <- igraph::distances(te.graph)
# te.graph <- igraph::simplify(te.graph)
# te.graph.comp <- igraph::components(te.graph)

names.new = unique(setdiff(c(b.graph[,1], b.graph[,2]), names(nodes.family.max)))
# names.new.val = paste('G',1:length(names.new), sep = '')
# names(names.new.val) = names.new
# names.new.val = 

names.new.family = sapply(names.new, function(s) strsplit(s, '\\|')[[1]][6])
names.new.family[names.new.family %in% c('DNA/Pogo', 'DNA/Tc1', 'DNA/Harbinger', 'DNA/En-Spm',
                     'DNA/HAT', 'DNA', 'DNA/Mariner')] = 'DNA'
names.new.family[names.new.family %in% c('RathE1_cons', 'RathE2_cons')] = 'DNA'
names.new.family[names.new.family %in% c('LINE/L1', 'LINE?')] = 'LINE'
names.new.family[names.new.family %in% c('Unassigned')] = 'Mix'


nodes.family.max.add = c(nodes.family.max, names.new.family)
nodes.family.max.add = nodes.family.max.add[unique(c(b.graph[,1], b.graph[,2]))]

graph.cols = discrete_rainbow(length(unique(nodes.family.max.add)))
graph.cols = sample(graph.cols)
names(graph.cols) = unique(nodes.family.max.add)

g.part <- network(b.graph, matrix.type = "edgelist", ignore.eval = FALSE, directed = TRUE)
p <- ggnet2(g.part, label = FALSE, edge.color = "black", node.size = 0.5, 
            color = nodes.family.max.add,
            palette = graph.cols, mode = "kamadakawai")
p
```

# TSNE
```{r}


library(Rtsne)




d <- igraph::distances(te.graph)
d.max = max(d[!is.infinite(d)])

d[is.infinite(d)] = d.max * 1.3

tSNE <- Rtsne(d, is_distance = TRUE, dims = 2)

plot(tSNE$Y[,1], tSNE$Y[,2])

```



